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Как мы исследовали энергоэффективность инференса нейросетей на планшете

Современные гаджеты невозможно представить без AI-функций. Размыть фон за спиной во время телеконференции, подавить шум от проходящих мимо коллег, вычесть «красные глаза» на фотографии — все это давно стало базовым функционалом любого устройства с камерой и микрофоном. Сейчас вендоры потребительских устройств устраивают настоящую «гонку вооружений», предлагая новые AI-функции, которые выделят устройство среди «собратьев».

Но у всех AI-функций есть цена, которую приходится «платить» конечному потребителю — более быстрая трата батарейки и перегрев устройства. В итоге производители электроники сталкиваются с дилеммой: фичи нужны, их нужно много, но без значительного ущерба для батарейки.

Меня зовут Павел Буровский, я инженер-разработчик ПО искусственного интеллекта. Вместе с Яной Булиной, инженером отдела проектирования новых поколений технологического стека департамента ИИ в YADRO, мы измеряли энергоэффективность выполнения некоторых AI-функций планшета KVADRA_T. В статье расскажем, как организовали необходимые эксперименты, и покажем много графиков с результатами запусков на CPU, GPU и NPU.

d8ae761dd4c8dbb75ef0b4a9439610a1.png

Анатомия AI-функций: что под капотом

Давайте рассмотрим типичный сценарий размытия фона за спиной у пользователя во время видеоконференции. Приложение получает с камеры изображение высокого разрешения и, используя сверточную нейросеть, определяет, где на изображении лицо человека. Дальше оно обрабатывает картинку: там, где фон, картинку размывает, там, где лицо, оставляет без изменений (или подает на вход следующей нейросети, если нужно).

Для нейросетей входное изображение нужно подготовить. Обычно они работают над изображениями небольшого размера и определенной цветовой кодировки. Так что на этом этапе приложение для видеоконференций должно:

  • отмасштабировать изображение к размерности входных данных нейросети,

  • переставить цветовые каналы местами,

  • сконвертировать целочисленные интенсивности цвета пикселов в каждом из цветовых каналов в вещественные числа (если инференс нейросети проходит в вещественных числах),

  • откалибровать интенсивности к интервалу [0,1].

Результат инференса следует обработать аналогичным образом до размытия фона: вернуть кодировку цветовых каналов, опять сделать resize к исходному разрешению и так далее.

Какие вычислительные устройства в этом задействованы

Теперь посмотрим, какие вычислительные устройства (в идеальном сценарии) задействованы и как данные движутся между ними.

Шаг 1. Сначала камера устройства формирует текущий кадр. В этот момент сырые данные с сенсора камеры попадают в ISP (Image Signal Processor). Там производится первичная обработка данных сенсора в изображении и очистка этого изображения от визуальных артефактов. Изображение готово, и передается из ISP в пространство памяти CPU, в запущенное приложение для видеоконференций. Некоторые устройства умеют передавать данные с ISP напрямую в NPU, но мы описываем общий случай и в такие детали уходить не будем.

Шаг 2. Стадия препроцессинга обработки кадра: приложение готовит изображение к инференсу. Для работы с изображениями исходно придумали GPU. Но решение об использовании видеокарты зависит от объема работы, который, в свою очередь, зависит от AI-функции. Если работы относительно немного, приложения могут обработать все на CPU, а не передавать изображения в GPU для короткой цепочки их трансформаций.

Шаг 3. Стадия инференса: обработанная картинка отправляется в NPU (Neural Processing Unit). Это специализированный ускоритель инференса нейросетей, который в теории должен проводить его быстрее и энергоэффективнее остальных устройств (проверим это на практике чуть позже). Если нам не хочется отправлять картинку на обработку в NPU или его нет в устройстве, инференс придется исполнить на том, что есть в наличии.

Шаг 4. Стадия постпроцессинга результатов инференса. Результат отправляется в пространство памяти CPU, где приложение опять производит цепочку трансформаций изображения, выбирая между CPU или GPU.

Шаг 5. Только на этом этапе готовое изображение с размытием фона наконец-то отправляется в видеопамять GPU для отрисовки картинки на экране.

Долгий путь изображения с камеры на монитор (в наиболее простом и массовом сценарии)
Долгий путь изображения с камеры на монитор (в наиболее простом и массовом сценарии)

Тут мы рассмотрели «под лупой», пожалуй, самый простой AI-сценарий. Бывают и более сложные случаи обработки одного видеокадра.

Например, на кадре нужно определить расположение лиц людей и каждое проверить на подлинность: настоящее ли оно или нарисованное на бумаге? Или даже фотореалистичное лицо, показанное с экрана другого устройства? Есть ли красные глаза и где они? А, может, сгладим неровности лица и нанесем макияж?

Кроме того, одновременно с видеопотоком есть аудиопоток. В нем может быть эхо от микрофона рядом с динамиком, звук пролетающего самолета, движущегося мимо автомобиля, или тот самый «непередаваемый» звук дрели в бетонных перекрытиях.

Во всех случаях данные могут бегать туда-сюда между вычислительными устройствами (CPU, GPU, NPU) много раз, а количество одновременно задействованных нейросетей может приближаться к паре десятков.

Выше мы писали, что для относительно несложной обработки изображений приложение может выбирать между двумя вычислительными устройствами — CPU или GPU. Проще не станет, если учесть, что и инференс нейросетей можно производить на любом вычислительном устройстве. А поскольку софт для инференса пишут один раз для всего «зоопарка» на рынке, то этот софт должен обеспечивать эффективное исполнение нейросетей на всех поддерживаемых устройствах.

Так, есть NNAPI — стандартный API для инференса нейросетей в Android/AOSP до версий 13. В зависимости от конкретного планшета или телефона и установленных драйверов, он может предлагать 2-4 вычислительных устройства на выбор. Это может быть CPU, GPU, NPU и даже DSP (Digital Signal Processor). Последний часто идет тандемом к NPU, и в некоторых устройствах может быть задействован как самостоятельный вычислительный блок.

Фреймворки для инференса и их влияние на производительность

Итак, инференс нейросетей можно производить на разных вычислительных устройствах. Для этого используют специальные фреймворки, которые реализуют инференс не только на каком-то одном устройстве, но и поддерживают гетерогенный запуск. Производительность инференса на CPU, GPU или NPU зависит не только от характеристик вычислительного устройства, но и качества софта для него.

На устройствах с Android или производных ОС на базе AOSP наиболее часто используемый фреймворк инференса — TFLite. В нем есть механизм «делегатов» — программных слоев поддержки различных вычислительных устройств. Благодаря нему несложно запускать инференс на CPU или встроенном GPU, выбрав соответствующий делегат. Производительность такого инференса будет зависеть не только от характеристик железа, но и от качества оптимизаций в конкретном делегате.

Поддержка NPU чуть более затруднена по нескольким причинам:

  • Не существует фреймворка, который бы поддерживал все необходимые NPU.

  • Для CPU есть C/C++, для GPU — OpenCL/Vulkan. Для NPU же нет единого языка программирования, чтобы сделать компилятор этого языка ответственным за поддержку железа NPU и сфокусироваться в таком фреймворке на логике инференса в едином для всех NPU делегате.

  • Каждый NPU обладает собственной архитектурой и поддерживает свое подмножество вычислительных слоев нейросетей. Вендоры NPU нередко предоставляют runtime к своему железу в бинарном виде, открывая только API и некоторое количество документации.

Все это делает эффективную поддержку производительности каждого конкретного NPU нетривиальной задачей, для решения которой часто нужна выделенная команда.

Для планшета KVADRA_T встроенный NPU идет со своим собственным вендорским фреймворком, в составе которого открытый API и закрытый рантайм. Изучением производительности такого NPU для эффективного применения в AI-нагрузках как раз и занимаются в отделе проектирования новых поколений технологического стека YADRO. Кроме поддержки NPU, целые команды оптимизируют инференс нейронных сетей на CPU и GPU — от программной оптимизации исполнения конкретных слоев до применения тензорных компиляторов.

Ранее наш коллега рассказал, как можно разработать тензорный компилятор для процессора на базе открытой архитектуры RISC-V с помощью OpenVINO и MLIR.

Как измерить энергоэффективность вычислений

Мы только что говорили о производительности — читай, времени работы конкретной нагрузки на конкретном вычислительном устройстве из числа доступных. Но в конечном счете важна не производительность сама по себе, а максимум функционала за минимальный расход батарейки. Производительность на это влияет: если быстрее выполнить вычисление, то есть шанс, что устройство быстрее уснет и не будет «есть» батарейку. Но влияет и выбор устройства для инференса.

Какое из доступных устройств планшета KVADRA_T — CPU, GPU или NPU — выгоднее использовать для инференса нейросетей в реальных сценариях? Давайте узнаем.

Для этого мы проведем эксперимент с исключением фонового энергопотребления от других подсистем планшета. Однако измерить энергоэффективность вычисления отдельных подсистем чипа нельзя. Для надежных данных требуется аппаратная поддержка измерения уровня расхода энергии каждого вычислительного блока на кристалле, и чаще всего ее нет. Поэтому нам придется поставить ряд косвенных экспериментов.

Методология бенчмаркинга

Эксперименты мы проведем на рядовом планшете KVADRA_T из массовой партии. На нем установлена отладочная прошивка kvadraOS, максимально приближенная к публичной версии kvadraOS 1.8.0. Она позволяет запускать консольные приложения в adb shell с правами root, чем мы и воспользуемся.

Теперь опишем, что мы учитываем в экспериментах.

Переведем устройство в авиарежим. Как устройство с радиомодулем планшет много работает даже в «покое», без активного вовлечения пользователя. И в зависимости от радиозагрязнения чаще или реже просыпается для обработки поступающих извне сигналов. Для исключения неконтролируемого энергопотребления радиомодулем мы переведем планшет в авиарежим. Интегральное исследование времени жизни планшета от батарейки с включенным радиомодулем в типичных пользовательских сценариях — тема отдельного исследования.

Будем держать экран всегда включенным. С точки зрения пользователя, при активном использовании планшета экран включен, а в период покоя — выключен. При выключенном экране мобильное устройство на базе kvadraOS уходит в сон и не выполняет полезной работы. Так что, проводя эксперименты, планшет нужно оставлять с постоянно включенным экраном. Расход энергии на него можно считать константой, одинаковой для всех экспериментов вне зависимости от загрузки вычислительных устройств. Запретим автоматическое отключение экрана в соответствующих настройках: Настройки → Экран и яркость → Время отключения экрана и выберем вариант Никогда.

Для оценки константной нагрузки от включенного экрана проведем две простых проверки.

Сначала зарядим планшет до 100%, выдернем шнур USB и дадим планшету полежать с выключенным экраном ровно 2 часа. У нас планшет за два часа разрядился на 1%. Так мы получили уровень потребления устройства в состоянии покоя. Значит, все потребление энергии, которое мы измерим потом, будет скорее связано с дополнительными нагрузками.

Затем запретим экрану выключаться, опять зарядим планшет до 100%, выдернем шнур USB и дадим планшету полежать уже со включенным экраном, тоже ровно 2 часа. На этот раз планшет разрядился на 9%. Если первая проверка, с выключенным экраном, проверяла, что нет катастрофических аномалий с железом, то эта проверка говорит, что эти 9%, собственно, накладные расходы от включенного экрана и активного CPU с запущенной на нем ОС этой версии.

Для калибровки наших измерений обратимся к специализированному измерительному стенду коллег из команды kvadraOS. Коллеги собрали его для детального изучения, сколько энергии под нагрузкой потребляют те или иные компоненты планшета. Стенд состоит из планшета (массовой партии), измерительной аппаратуры и тестовых линий от измерительного прибора к точкам подключения на материнской плате планшета.

Проводки из отверстий планшета — это как раз те тестовые линии от измерительного прибора к точкам подключения на материнской плате
Проводки из отверстий планшета — это как раз те тестовые линии от измерительного прибора к точкам подключения на материнской плате

Коллеги, которые разрабатывают kvadraOS, подтверждают, что наши измерения соответствуют наблюдениям на стенде.

Дисклеймер: дальнейшие обновления kvadraOS могут поменять полученные значения достаточно существенно. За время проведения экспериментов коллеги доработали части операционной системы, влияющие на энергоэффективность, и работа по улучшению kvadraOS, естественно, продолжится. Но для нашего эксперимента достаточно такого замера.

Метод проведения запусков. Для экспериментов мы будем использовать консольное приложение. Но как делать запуски в adb shell, если планшет переведен в авиарежим? Для этого придется подключаться к планшету по USB. Но и тут есть проблема: подключение по USB дополнительно заряжает батарейку.

При каждом запуске приложения мы:

  1. Стартуем с полного заряда, чтобы USB-подключение в момент запуска не добавляло неконтролируемый заряд батарейке.

  2. Ставим на 2 часа запуск персистентного процесса с помощью утилиты nohup и быстро-быстро вытаскиваем USB-шнур.

  3. Затем приходим через два часа, снова подключаемся по USB и инспектируем логи.

Условные 5 секунд, которые планшет в начале эксперимента был еще подключен по USB, дают пренебрежимую погрешность измерений энергопотребления на фоне 2 часов времени работы одного теста. А если в начале эксперимента в скрипте поставить sleep 5 –, то тестируемое приложение даже не начнет запуск, когда мы вытащим USB-шнур. За это время CPU не успеет раскрутиться на полную, и энергопотребление при подключенном USB будет минимальным.

Небольшое отступление про «научность метода» сбора данных

Обычно методология бенчмаркинга производительности подразумевает детальный контроль тактовых частот CPU, привязки процессов к конкретным ядрам CPU и исключение всех внешних «раздражителей». Иначе невозможно надежно отличить собственно исследуемую нагрузку от «фоновой». Ведь исследуемое приложение живет не в вакууме, а в операционной системе, активирующей различные фоновые процессы. К тому же в реальности пользователь может (и обязательно будет) одновременно запускать несколько приложений.

Тут мы попробуем проплыть между двумя крайностями.

С одной стороны, у нас не микробенчмаркинг. Строго контролируемый, научный подход к бенчмаркингу мы не сможем воспроизвести. Наши нагрузки — сложные, большие приложения, состоящие из нескольких фреймворков. И запускаемое приложение вместе со всеми библиотеками тянет на полгигабайта бинарных файлов. Мы не сможем реализовать нагрузки, похожие на реалистичный пользовательский сценарий, в виде изолированных микробенчмарков. И, стараясь сделать процесс запусков приближенным к пользовательскому сценарию, мы не должны ограничивать тактовые частоты CPU и окружение. Точно так же это не будет делать конечный пользователь. Поэтому наши измерения неизбежно будут «плыть» — вопрос лишь в том, насколько сильно.

С другой стороны, мы не делаем интегральное пользовательское тестирование, которое часто можно увидеть в рецензиях новых гаджетов: техноблогер неделю ходит с девайсом под мышкой на работу и в сауну, стараясь использовать все фичи. У нас задача изолированная: оценить, можно ли достаточно уверенно выбрать конкретный вычислительный девайс (CPU, GPU или NPU) у планшета KVADRA_T для определенного класса нагрузок.

Так как мы проводим относительное сравнение, а не замер абсолютных показателей, в нашем эксперименте мы лишь делаем однотипные запуски повторяющегося вычисления и смотрим на разряд батареи. Проснулась ОС больше или меньше раз? Поднялась ли тактовая частота CPU тут и там? Если делать однотипные запуски на несколько часов, «дрожания» железа и софта выровняются и сгладятся, а влияние «внешних раздражителей» станет более-менее похожим на константное. Для экспериментов, описанных в статье, мы выбрали два часа как минимальное время запуска теста для усреднения «шумящих» показателей (и максимальное для нашего терпения).

Метод контроля разряда батарейки. Расход мы будем контролировать с помощью logcat. В adb shell запустим:

logcat -d | grep healthd | grep = | tail -n 2

Получим строки вида:

08-29 02:35:11.959   485   485 W healthd : battery l=81 v=8110 t=34.5 h=2 st=3 c=-512000 fc=9000000 chg=
08-29 02:36:41.815   485   485 W healthd : battery l=81 v=8104 t=34.5 h=2 st=3 c=-512000 fc=9000000 chg=

Тут нас интересуют:

  • дата 08-29 02:35:11.959 (месяц, день, час, минуты, секунды),

  • l = 81 — 81% заряда батареи,

  • c = –512000: отрицательное значение — идет разряд батареи, положительное — батарея заряжается, ноль — батарея заряжена.

Контроль значений из logcat будем делать в самом начале и конце персистентного процесса (persistent process), запускаемого с помощью nohup. В результате будем смотреть на разницу в заряде батареи.

Скрипт запуска нагрузочного теста будет выглядеть примерно так:

echo "sleep 5s"
sleep 5s

echo "Fingerprint:"
getprop ro.vendor.build.fingerprint

echo "time before benchmarking"
date

echo ${EXPERIMENT_NAME}
echo 
echo "energy before benchmarking: "
logcat -d  | grep healthd | grep = | tail -n 2
echo

export RUN="./${APP} ${RUN_OPTIONS} ${THREADING_OPTIONS}"
echo ${RUN} ${LIMIT}
${RUN} ${LIMIT}

echo "time after benchmarking:"
date

echo
echo "energy after benchmarking:"
logcat -d  | grep healthd | grep = | tail -n 2

Запускать его будем так:

nohup ./run_script.sh &

Тестовое приложение для нагрузочного тестирования

Для экспериментов возьмем демонстрационное приложение распознавания лиц, функционально эквивалентное сценарию разблокировки планшета по изображению с камеры (face unlock). Обработка каждого изображения из входящего потока состоит из трех стадий:

  • Детекция лица (face detection) — поиск лиц на картинке.

  • Распознавание лиц (face recognition) — для каждого найденного лица: распознавание относительно заготовленных образцов.

  • Проверка подлинности лица (face anti-spoofing, antispoof) — для каждого найденного лица: защита от подделок, проверка на аутентичность. Планшет должен уметь определять, что к нему подошел истинный владелец, а не злоумышленник с его портретом.

Каждая стадия обработки входящего изображения состоит из:

  • препроцессинга изображения (строго на CPU),

  • собственно инференса (на CPU, GPU или NPU),

  • постпроцессинга (строго на CPU).

Пре- и постпроцессинг изображений делается полностью на CPU. В нашем приложении эти стадии обработки кадра занимают несущественное время (доказывающие это цифры и графики будут в разделе с результатами), и от применения GPU скорее станут медленнее.

А вот инференс для каждой стадии в этом приложении можно отправлять на любое из доступных устройств. Для инференса на CPU и GPU используется TFLite 2.17.0, а на NPU работает вендорский фреймворк (для конкретности ниже его будем обозначать аббревиатурой VFNPU). Стадии обработки кадра используют публичные модели топологий blaze_face, ghostfacenet и minifasnet в форматах .tflite и бинарном формате VFNPU (результат нашей конверсии .tflite моделей в этот формат, используя конвертер моделей из пакета VFNPU). С поддержкой ghostfacenet, в целом, возникли проблемы, которые мы опишем позже.

Размер моделей и их глубина (в формате .tflite):

  • blaze_face: объем данных порядка мегабайта, 353 скрытых слоя,

  • ghostfacenet: 15.5MB, 241 слой,

  • minifasnet: 1.7 MB, 361 слой.

При этом «сложность» инференса существенно варьируется от слоя к слою. Также для каждого типа слоев она зависит от мета-параметров этого слоя: размерности сверток, наличия нетривиальных страйдов и так далее.

В разных фреймворках инференса каждый слой нейросети для каждого типа вычислительного устройства оптимизирован по-разному. Поэтому заранее точно предсказать производительность инференса по глубине сети или ее архитектуре (топологии сети, используемым слоям) почти никогда нельзя.

Есть нюанс в типах данных. Модели для TFLite выполняются в точности fp32, но этот тип данных нативно не поддерживается железом NPU — для него модели конвертированы в fp16 фреймворком от вендора.

В рамках эксперимента мы не стали сравнивать инференс моделей, «квантизованных» нами в различные типы данных (fp16, int16, int8, ...). Для этого потребовалось бы дополнительно проверять качество квантизации. Грубо говоря, проверять, что модель, исходно рассчитанная при инференсе в fp32 определять на изображении котиков, после квантизации в int8 все еще определяет котиков. А это выходит за рамки статьи.

Тем не менее, TFLite позволяет производить наиболее вычислительно нагруженные части вычисления в fp16 при входной модели с весами в fp32. Это некоторый компромисс. Пользователь не должен производить предварительные манипуляции с входной моделью, конвертируя ее из fp32 в fp16. При этом TFLite производит конверсию между fp32 и fp16 «на лету», достигая двукратных ускорений на тех CPU, которые уже поддерживают fp16. Подробнее — здесь.

Выбранное для теста приложение — консольное. Оно должно максимально нагрузить вычислительные устройства работой в сценарии, приближенном к настоящему, но при этом исключить влияние смежных устройств и библиотек. Например, мы не хотим учитывать время на захват кадра с камеры и копирование данных этого кадра между различными HW-устройствами и слоями API в ОС. Поэтому мы заранее приготовили подборку фотографий с различным количеством лиц на каждой (в среднем по одному лицу на изображение) и при запусках будем циклически пробегаться по ней.

Приложение принимает аргументы командной строки, с помощью которых можно:

  • Задать время работы эксперимента (например, 2 часа).

  • Для каждой из трех стадий по отдельности: проводить ли инференс на CPU, GPU или NPU,

  • Для каждой из трех стадий по отдельности: какое количество потоков использовать на выбранном устройстве. Можно и не указывать, тогда используются настройки по умолчанию.

Пример запуска

Перед тем как перейти к результатам экспериментов, покажем, как они у нас получались. Для примера рассмотрим детальный лог одного из запусков. Этот файл — содержимое файла nohup.out, который можно найти в директории запуска после команды nohup ./run_script.sh &.

Детальный лог одного из запусков
sleep 5s

Fingerprint:
Kvadra/ts11_consumer/ts11:12/kvadraOS_Nightly_Release_2024-08-21/builduser08210506:userdebug/release-keys

time before benchmarking
Sat Aug 24 21:12:05 MSK 2024

Running inferences on: CPU (blazeface, tflite, 4cores), NPU (ghostfacenet, auto), NPU (minifasnet, auto)

energy before benchmarking: 
08-25 15:44:58.686   485   485 W healthd : battery l=100 v=8650 t=27.5 h=2 st=3 c=320000 fc=9000000 chg=
08-25 15:45:01.358   485   485 W healthd : battery l=100 v=8518 t=28.0 h=2 st=3 c=-256000 fc=9000000 chg=

./face_recognition_demo_omit-frame-pointer -dfd CPU -dreid NPU -das NPU --nthreads_fd=4 -i data/images --loop --noshow --detailed_report --db data_base.json --time 7200
[ INFO ] Create face detection model
[ INFO ] Inference adapter: TFLite 2.17.0
[ INFO ] Loading model to CPU device
[ INFO ] Preparing inputs/outputs for model
[ INFO ] 	Model inputs:
[ INFO ] 		input: FP32 [1,192,192,3]
[ INFO ] 	Model outputs:
[ INFO ] 		reshaped_classifier_face_4: FP32 [1,2304,1]
[ INFO ] 		reshaped_regressor_face_4: FP32 [1,2304,16]
[ INFO ] Create face recognition model
[ INFO ] Loading model to NPU device
[ INFO ] 	Model inputs:
[ INFO ] 		serving_default_input_5:0: FP16 [1,112,112,3]
[ INFO ] 	Model outputs:
[ INFO ] 		StatefulPartitionedCall:0: FP16 [1,512]
[ INFO ] Initialize antispoof model
[ INFO ] Loading model to NPU device
[ INFO ] 	Model inputs:
[ INFO ] 		input: FP16 [1,3,80,80]
[ INFO ] 	Model outputs:
[ INFO ] 		Identity: FP16 [1,3]
[ INFO ] Time limit: 7200 sec
[ INFO ] 
[ INFO ] Total elapsed time: 7200.1 s
[ INFO ] Total processed frames: 125471
[ INFO ] Total metrics report:
[ INFO ] 	Latency: 56.1 ms
[ INFO ] 	FPS: 17.4
[ INFO ] 
[ INFO ] Face Detection Stage
[ INFO ] ---------------------
[ INFO ] Total Latency: 9.99 ms
[ INFO ] Average Detections Count: 1.0
[ INFO ] BlazeFace metrics report:
[ INFO ] Average Latency: 9.92 ms
[ INFO ] 	Pre-processing : 1.73 ms
[ INFO ] 		- Color Convert       : 0.15 ms
[ INFO ] 		- Create Tensor       : 0.45 ms
[ INFO ] 		- Interop             : 0.14 ms
[ INFO ] 		- Normalize           : 1.60 ms
[ INFO ] 		- Resize              : 1.43 ms
[ INFO ] 	Inference      : 8.16 ms
[ INFO ] 	Post-processing: 0.02 ms
[ INFO ] 
[ INFO ] Antispoofing Stage
[ INFO ] ---------------------
[ INFO ] Total Latency: 5.03 ms
[ INFO ] Average Inferences Count: 1.0
[ INFO ] MiniFASNetV2 metrics report:
[ INFO ] Average Latency: 4.80 ms
[ INFO ] 	Pre-processing : 0.97 ms
[ INFO ] 		- Create Tensor       : 2.55 ms
[ INFO ] 		- Crop                : 425.97 ms
[ INFO ] 		- Interop             : 3.16 ms
[ INFO ] 		- Resize              : 157.63 ms
[ INFO ] 		- Transpose           : 35.88 ms
[ INFO ] 	Inference      : 3.83 ms
[ INFO ] 	Post-processing: 0.00 ms
[ INFO ] 
[ INFO ] Face Recognition Stage
[ INFO ] ----------------------
[ INFO ] Total Latency: 14.97 ms
[ INFO ] Average Inferences Count: 1.0
[ INFO ] GhostFaceNet metrics report:
[ INFO ] Average Latency: 14.31 ms
[ INFO ] 	Pre-processing : 1.00 ms
[ INFO ] 		- Color Convert       : 14.95 ms
[ INFO ] 		- Create Tensor       : 5.88 ms
[ INFO ] 		- Interop             : 1.83 ms
[ INFO ] 		- Warp Affine         : 618.56 ms
[ INFO ] 	Inference      : 13.31 ms
[ INFO ] 	Post-processing: 0.00 ms
[ INFO ] 
[ INFO ] Re-ID Stage
[ INFO ] ----------------------
[ INFO ] Total Latency: 0.04 ms
[ INFO ] Number of faces in data base: 2
[ INFO ] 
[ INFO ] Resources usage:
[ INFO ] 	Mean core utilization: 11.7% 8.8% 8.9% 6.2% 17.7% 32.5% 14.7% 32.0% 
[ INFO ] 	Mean CPU utilization: 16.6%
[ INFO ] 	Memory mean usage: 2.0 GiB
[ INFO ] 	Mean swap usage: 0.0 GiB

time after benchmarking:
Sat Aug 24 23:12:06 MSK 2024

energy after benchmarking:
08-25 17:44:49.835   485   485 W healthd : battery l=72 v=7952 t=33.0 h=2 st=3 c=-512000 fc=9000000 chg=
08-25 17:44:58.637   485   485 W healthd : battery l=72 v=7930 t=33.0 h=2 st=3 c=-512000 fc=9000000 chg=

Рассмотрим этот лог сверху вниз:

  1. Видим так называемый fingerprint, то есть строку-идентификатор сборки операционной системы планшета. Это полезно для отслеживания логов во время эксперимента (пока мы готовили статью, сменилось несколько прошивок kvadraOS).

  2. Текущая дата эксперимента — обратите внимание на различие в дате и времени от команды date и в логах healthd (пожимаем плечами, идем дальше).

  3. Затем лог уточняет, как именно было запущено нагрузочное тестирование. face detection — на CPU в 4 потока с помощью фреймворка TFLite, face recognition и antispoof — на NPU с помощью вендорского фреймворка в автоматических настройках распределения нагрузки по ядрам NPU.

  4. Далее идет фрагмент лога демона healthd, запущенного до нагрузочного тестирования: лог показывает, что запуск стартует со 100% заряда батареи, но разряд батареи уже начался (значение c = –256000 отрицательное).

  5. Следом видим большой блок запуска нагрузочного теста, за которым идет еще один вывод команды date и фрагмент лога healthd, собранного сразу после вычисления.

  6. Из последних строк лога видно, что вычисление закончилось в 23:12:06 (начавшись в 21:12:05 и заняв два часа) и батарейка разрядилась до 72%.

Заметьте, что бинарное имя консольного приложения содержит omit-frame-pointer. Мы собирали приложения двумя способами, в том числе с опцией -fno-omit-frame-pointer, которая полезна для профилирования приложения и раскрутки стека. Во время «пристрелочных» запусков мы проверили профайлером, что во время запуска на системные вызовы и активность ОС было потрачено пренебрежимо малое количество времени. Запуски, вошедшие в результаты эксперимента, проводились без профайлера и с использованием сборки без опции -fno-omit-frame-pointer.

Тест при запуске выдает детальный лог, показывающий, например, следующее:

  • запуск шел с ограничением по времени в 7200 сек,

  • за это время было обработано 125 471 кадра,

  • достигнуто значение метрики FPS 17.4. Проверка: делим 125 471 кадр на 7 200 секунды, получаем 17.42.

  • затем идет детализация запуска для трех стадий обработки кадра; из нее следует, что, например, в случае face detection препроцессинг составил 1.73 мс, постпроцессинг — 0.02 мс, доминировал в запуске инференс в 8.16 мс.

  • средняя загрузка CPU при запуске не превышала 17%, что логично, учитывая относительно большое время, потраченное на инференс на NPU.

Обратите внимание, что запуск модели ghostfacenet доминирует по сравнению с инференсом двух других моделей. Было бы здорово объяснить это архитектурой нейросети, но тут начинается сюжет другой статьи. Также интересно, что суммарная задержка на обработку кадра (значение Latency 56.1 мс в разделе Total metrics report) больше, чем сумма задержек трех стадий обработки кадра: куда-то «делись» 56.1 − 9.99 − 5.03 − 14.97 = 26.11 мс. Фактически они ушли на чтение кадра, копирование данных, рендеринг изображения и прочие накладные расходы.

Не удивляемся: у всякого вычисления есть накладные расходы, они же пространство/возможности для оптимизаций. Выбранное приложение не претендует на оптимальную производительность, это скорее mock функционала разблокировки планшета по лицу. При реализации рассматриваемой AI-функции в каком-то другом приложении и накладные расходы будут другими. Однако в нашем эксперименте такие дополнительные задержки делают менее очевидной связь между производительностью отдельных стадий вычисления и итоговым энергопотреблением.

Результаты

Мы провели запуски приложения в следующих комбинациях:

5f2897aac45dee5b1b38faaf8498a3f9.png

Запуски на CPU мы проводили для 1, 2 и 4 потоков. Мы не управляли тактовыми частотами и привязкой потоков к ядрам, потому что этого не будет делать пользователь.

Мы отдаем себе отчет, что в конечном высоконагруженном приложении с большим количеством AI-функций эти нагрузки могут быть размещены на совсем других ядрах. А еще — за время исполнения приложения они бы могли мигрировать с одних ядер на другие (вспоминаем, что у современных мобильных big.LITTLE-устройствах, как правило, CPU-ядра разной производительности). У нас нет цели рассмотреть в рамках статьи все возможные сценарии.

Также для запусков на CPU мы сравнивали инференс в fp32 — нативной точности исходной модели — и в fp16. Для fp16 мы модель не квантизовали, но использовали параметр нашего консольного приложения для вызова XNNPACK-делегата TFLite, предоставляющего возможность принудительного инференса в fp16 независимо от метаданных модели. В таком случае данные «на лету» конвертируются из fp32 в fp16.

При выборе параметров для наших запусков мы столкнулись с несколькими проблемами, связанными с ограниченной поддержкой модели ghostfacenet для стадии face recognition. Инференс для нее завершается аварийно в двух случаях:

  • при запуске с GPU делегатом TFLite,

  • при запуске с XNNPACK в качестве CPU-делегата TFLite в точности fp16.

Изучение причин падений и их исправление — не тема этой статьи. Мы просто отбросили все комбинации неуспешных запусков. Поэтому, запуская другие модели на GPU, инференс именно этой модели мы проведем на CPU или NPU. И при запусках на CPU используем референсный CPU-делегат.

Для запусков на GPU мы использовали настройки по умолчанию. Оптимальным подбором параметров запуска на GPU мы не занимались по двум причинам. Во-первых, это отдельная большая работа. Во-вторых, как мы уже писали, для этой статьи нас интересовала поддержка вычислительных устройств фреймворками «из коробки», без тонкой доводки.

При инференсе на NPU мы различали следующие запуски:

  • в настройке по умолчанию,

  • запуск всего инференса на одном ядре NPU,

  • запуск на трех ядрах сразу.

Пояснение. Фреймворк от вендора различает четыре вида управления потоками: автоматический, где распределение нагрузки между тремя ядрами NPU определяется фреймворком, и три режима распределения нагрузки между одним, двумя и тремя ядрами сразу. Тут мы проверили три варианта из четырех.

Время обработки одного кадра

Сначала посмотрим, куда тратится время в процессе обработки одного кадра. На примере запуска H, лог которого мы уже приводили в предыдущей секции:

22e47d990efcbab87a18cc7121596047.png

Здесь цветовая гамма (синий, оранжевый, серый, зеленый) соответствует цветовой гамме диаграмм, приведенных в следующем разделе:

  • зеленый обозначает инференс на NPU,

  • серый — инференс на CPU,

  • остальные цвета (синий, оранжевый) — исполнение различных подготовительных и иных вычислений на CPU.

  • розовый — суммарные накладные расходы на обработку кадра, специфичную для этого демо-приложения, которую мы обсуждали в предыдущей секции.

Сравнение производительности инференса на различных устройствах

Следующие три графика для каждой стадии обработки кадра (face detection, face recognition, antispoof) сравнивают производительность инференса на различных вычислительных устройствах (CPU, GPU и NPU). Для наглядности, кроме времени инференса, мы также добавили время выполнения препроцессинга и постпроцессинга соответствующей стадии обработки кадра.

1f6dcfaae9757bd786819cefcf65bf92.png37b47ddb8e88e5b00b145598eabb69db.png5a816e4bd30b8e03f7f295c96f7873f6.png

Что мы видим на этих графиках?

Во-первых, для всех трех стадий обработки кадра доминирует инференс, а производительность инференса моделей для разных стадий измеримо отличается. Посмотрим на запуск E: для face detection инференс составляет 5.45 мс, для face recognition — 13.7 мс, для antispoofing — 3.92 мс.

Во-вторых, разница в производительности инференса для «хороших» (более быстрых) и «плохих» (более медленных) запусков составляет в среднем 3-4 раза. Сравним запуски О и Е для face detection: время работы инференса 14.11 мс против 5.45 мс — разница в 2.5 раза. Для face recognition разница между запусками A и M составляет уже 3.2 раза, а для antispoofing разница между запусками О и H и вовсе 4.4.

Для antispoofing и face recognition виден существенный разрыв в производительности между «хорошими» и «плохими» запусками: почти все «плохие» столбики очень высокие, а «хорошие» — низкие. Для face detection это менее заметно, тут спектр значений более равномерный.

Также для AI-сценария разблокировки планшета по изображению с камеры препроцессинг составляет небольшую (чаще — незначительную) часть вычисления, а постпроцессинг и вовсе вносит микроскопический вклад (NB: для других AI-сценариев это может быть совсем не так). Лишь для face detection препроцессинг составляет сколько-нибудь значимую часть времени от обработки кадра. Мы даже убрали числовые значения для постпроцессинга с графиков для antispoofing и face recognition: значения там не более 0.01 мс.

Соотношение FPS и разряда батарейки

Но время работы каждой стадии обработки кадра, к сожалению, не влияет напрямую на время работы от батарейки. Если смотреть на эксперимент целиком, во всей его двухчасовой длительности, данные выглядят так:

ab8c6d13b48f5395448fd214b3dce438.png

Такое отображение данных скорее мешает понять происходящее, чем помогает. Для метрики FPS чем больше значение, тем лучше. А для метрики Discharge (разряд батареи) наоборот. Из-за этого трудно на глазок сравнивать запуски друг с другом. Тем не менее, отсюда видно, что запуски с бо́льшим значением FPS могут давать худшее значение разряда. Иными словами, быстрее не всегда значит лучше для батареи. Например, FPS у запуска E (значение 18.0) выше, чем у запуска F (значение 9.7), но для времени жизни от батареи запуск F лучше (20% разряда против 23.5%).

Для лучшей наглядности построим график с этими двумя метриками по разным осям и обозначим для полученных точек фронт Парето.

22ece1f8c00fc2d565afdf832cede3b6.png

Такие диаграммы строят, когда есть две равнозначно полезные метрики и нужный пользователю баланс между показателями этих метрик зависит от контекста. Невозможно заранее выбрать, что пользователю важнее: максимальная производительность (FPS) с неважно каким значением разряда (battery discharge) или, наоборот, минимальный разряд при неважно каком значении производительности.

Каждый запуск, дающий пару (FPS, значение разряда батареи), изображается точкой на диаграмме. При этом идеальный запуск — это такой, для которого значение FPS максимально, а разряд батареи минимален — обозначался бы точкой в правом нижнем углу диаграммы.

Все точки, оказывающиеся выше и левее очерченной огибающей наших измерений (так называемому фронту Парето), заведомо хуже точек, которые попали на эту огибающую. Те запуски, что выше и левее фронта Парето, в равной степени неудовлетворительны и по значению разряда, и по значению производительности. В то же время запуски, попавшие на фронт Парето, оказываются в равной степени неплохи — как минимум до тех пор, пока пользователь не озвучит свои предпочтения.

К каким выводам и ответам нас приводит диаграмма

Запуски с самым быстрым инференсом в отдельных стадиях (запуск Е для face detection, запуск M для face recognition и запуск H для antispoofing) не попали на фронт Парето. Быстро в отдельных стадиях — не всегда хорошо для запуска в целом.

Помогает ли энергоэффективности использование GPU?

Запуски K, L и M c использованием GPU даже близко не подходят к фронту Парето. По «плохости» они сравнимы с запуском A (CPU работает в 1 поток на всех трех) и запусками S, H и C (во всех трех запусках CPU обрабатывает инференс как минимум одной модели в 4 потока, производя вычисления в fp32).

Вывод: GPU этого конкретного планшета, с текущим драйвером и качеством оптимизаций в GPU-делегате TFLite, скорее мешает экономить батарею, чем помогает.

Помогает ли переход от fp32 к fp16 при инференсе на CPU?

  • Запуск D против E: запуск Е с вычислением в fp16 оказался прямо на фронте Парето — он и быстрее, и менее прожорливый для батареи.

  • F против G: ничего сказать нельзя — оба плохи.

  • H против I: запуск I оказался на фронте Парето.

  • Q против R: ничего сказать нельзя — оба плохи.

  • S против T: запуск T оказался чуть менее прожорливым для батареи.

Вывод: инференс в fp16 скорее помогает, чем нет.

Сколько ядер CPU лучше использовать?

  • Сравним запуски R и T: запуск R, исполняющий antispoofing в два потока, оказался более удачным, чем запуск T, делающий то же самое в четыре потока.

  • В паре запусков K и L хуже запуск, исполняющий face recognition в четыре потока, — хотя, для справедливости, оба запуска плохи (очень далеки от правого нижнего угла диаграммы).

  • В паре запусков A и C аналогичный вывод сделать нельзя: у запуска А лучше энергоэффективность, но хуже показатель FPS, у запуска C — наоборот. Они несравнимы.

Вывод: Запуск в один поток явно плох. Однозначный выбор между запусками в два и четыре потока сделать нельзя. Пояснительная бригада: закончить вычисление на CPU побыстрее и дать CPU побыстрее заснуть — внешне разумная идея, объясняющая неудачу запусков в один поток (у CPU велико статическое потребление энергии, запуск в один поток просто оттягивает уход в сон). Но выигрыш во времени запуска, в отличие от электропотребления, растет не пропорционально числу используемых потоков. Для подробных и точных выводов нам тут не хватает данных. Для этой статьи ключевое наблюдение в том, что CPU хотя бы иногда, для некоторых моделей, оказывается разумным выбором по сравнению с NPU — об этом чуть ниже.

Помогает ли управление числом ядер для данного NPU и его фреймворка?

  • Точно не помогает выбор всех трех ядер NPU: запуски F, G и О проводились именно так — итог печальный. Запуск О оказался на фронте Парето, но при одинаковом разряде с запуском N (17%) запуск О дает практически вдвое меньший FPS, чем запуск N. А нам FPS и так не хватает.

  • Запуски N и P оказались на диаграмме очень близки друг к другу. Запуск P, проводившийся с указанием одного ядра NPU, оказался чуть лучше запуска N, проводившегося с указанием автоматического распределения нагрузки между ядрами NPU.

  • Аналогично, запуски E и I оказались весьма близки друг к другу на диаграмме — тут разница в позициях на графике может быть близка к порядку ошибки измерений.

Вывод: В нашем эксперименте запуски нагрузки на трех ядрах NPU дали стабильно неудовлетворительные результаты. А вот использование одного ядра или автоматическая балансировка нагрузки показали себя одинаково хорошо.

Какие запуски в итоге попали на фронт Парето и оказались максимально близки к нему?

  • Запуски, максимально задействующие NPU (O, N, P) для всех трех стадий обработки кадра: минимальный разряд батареи (17–18%) и инференс порядка 15 FPS (для N и P).

  • Запуски E и I, оставляющие face detection на CPU, а остальные две стадии — на NPU: тут разряд батареи порядка 24%, но и частота обработки кадра подросла до 18 FPS.

Добавим к этому, что разница в пару FPS вряд ли окажет существенное влияние на user experience. Получили 15 FPS или 18 FPS при обработке кадра — это все равно примерно вдвое меньше показателя, при котором пользователь вообще не заметит процесса обработки (для этого нужно как минимум 30 FPS). А вот 15 FPS против 8.6 FPS — это уже почти вдвое лучше. Вот так мы только что озвучили свои экспертные предпочтения для выбора «наилучшей» точки на фронте Парето.

Наилучшими запусками стали N и P, дающие 15 FPS и 17–18% разряда батареи за 2 часа эксперимента. В запусках N и P мы используем только NPU. Если же мы хотим использовать CPU для инференса, то наилучший вариант использовать CPU — в 4 потока, в точности fp16 и только для стадии face detection с моделью blaze_face.

Также интересно, что запуски c NPU стабильно более энергоэффективны. Несмотря на то, что задержка в случае обработки инференса на стадии face detection на NPU выше (12.19–14.11 мс), чем на CPU (от 5.45 мс)!

В заключение

Для инференса нейросетей придумали специальное устройство — NPU, и в большинстве случаев выполнять его желательно именно на нем.

Мы с помощью экспериментов проверили, что настройки по умолчанию для фреймворка NPU от вендора работают хорошо. При выборе режима запуска нагрузок на NPU точно не стоит делать запуск на трех ядрах NPU. Иногда запуск инференса в одно ядро NPU помогает, но существенного прироста производительности и энергоэффективности это не даст.

Наш эксперимент показывает, что эффект от использования GPU c GPU-делегатом фреймворка TFLite 2.17.0 на планшете KVADRA_T строго отрицательный (если мы оцениваем энергоэффективности таких запусков). Возможно, будущие оптимизации GPU-делегата исправят ситуацию, однако в нашем случае для этого как минимум необходима поддержка модели ghostfacenet и ей подобных.

Мы также подтвердили экспериментально, что во всех случаях использовать NPU выгоднее для энергоэффективности. Но, если очень хочется «поднажать», для некоторых нейросетей можно использовать CPU: чуть выиграем в FPS, чуть проиграем во времени жизни планшета от батареи.

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    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.06.26 21:13 Emilie Safi

    A fraudulent investment scheme operated by BTCMining.limited functions as a fake return scam. In this setup, scammers lure victims with false promises of high returns. Through manipulative tactics, they gain individuals' trust and convince them to invest, ultimately leading to financial loss. If you have ever faced a cyber threat or fallen victim to an online crypto scam and need to reach the authorities, I recommend contacting [email protected], [email protected], WhatsApp +19852969146, telegram @resqprofirm. They are a legitimate team that helps victims of online crypto scams using advanced tools.

  • 25.06.26 21:25 Emilie Safi

    So I ended up losing $38,000 to this platform. At first, they kept asking me to put in more money so I could get into my portfolio. I did that, but then they wouldn’t let me withdraw anything—just kept asking for more deposits. It got way too suspicious, so I stopped. I found this company called ResQProfirm on Google and told them what happened. They got in touch, asked me to walk them through everything, and I gave them all the proof I had. They did an amazing job tracking down my money and getting it back. Big thanks to them at [email protected] and on WhatsApp at +19852969146. Please be careful out there and always research before investing.

  • 26.06.26 01:04 robertalfred175

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  • 26.06.26 01:04 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 26.06.26 02:48 Miriam Rocha

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  • 26.06.26 02:52 Miško Bakić

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  • 26.06.26 02:56 Asunción Herrera

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  • 26.06.26 15:05 Riley Stephens

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  • 28.06.26 00:37 kimberlyhebertt673

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  • 28.06.26 00:37 kimberlyhebertt673

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  • 29.06.26 22:37 riley777

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  • 30.06.26 15:08 wendytaylor015

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  • 30.06.26 15:08 wendytaylor015

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  • 02.07.26 01:22 Lieneke Bonnema

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  • 02.07.26 01:26 Clara Morin

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  • 04.07.26 15:32 Fraddy Pual

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  • 05.07.26 14:44 lydiassmith567

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  • 05.07.26 14:44 lydiassmith567

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  • 06.07.26 16:20 Olga Ognjanović

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  • 06.07.26 16:31 Joseph Weigl

    Invest wisely and stay cautious. Don't be influenced by promises of unusually high returns or convincing sales pitches from brokers. I learned this the hard way after falling victim to an investment scam that promised huge profits. Fortunately, I acted quickly and reported the incident to a recovery firm for assistance. Contact: Email: [email protected] Telegram: @Resqprofirm WhatsApp: +1 985 296 9146

  • 06.07.26 16:33 Jaran Løvlien

    A heartfelt thank you to RESQPRO FIRM for their commitment and professionalism throughout the investigation of my case. Their team worked diligently and helped recover assets valued at $88,000, which were returned to my wallet. I truly appreciate their support, clear communication, and dedication, and I'm grateful for the assistance I received. Contact: Email: [email protected] Telegram: @Resqprofirm WhatsApp: +1 985 296 9146

  • 07.07.26 18:00 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 07.07.26 18:01 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 09.07.26 19:06 Toivo Walli

    I lost 8.56btc to a fake Bitcoin mining site, I tried withdrawing but couldn't approved my process, I reported to !R£SQPROFIRM! via °R£SQproFirm°àt°gmail•com° °tEL£°gram=R£SQprofirm °whaT°Zap+198°52°96°91°46

  • 09.07.26 19:10 Misty Alexander

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  • 09.07.26 19:13 Clara Soto

    Anyone receiving continued requests for additional deposits from a scam platform should immediately cut off communication and submit the case to a reputable re­covery service for investigation. R£sQprofirm is a dependable firm you can reach at °ResQproFirm°àt°g,*ma'il(•)¢om°, TEL£gram ResQprofirm, or |whaTZap| +1-985-296-9146.

  • 12.07.26 03:30 Kora Baltacha

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  • 12.07.26 03:33 Pahal Mathew

    It's important to move proactively by engaging an experienced recovery specialist. They will assist you throughout the process. To help them, provide: · Transaction evidence · Scammer information · Any additional relevant details The experts will then track and try to retrieve your funds from the scammers' hidden accounts or wallets. R£sQprofirm offers recovery assistance with no upfront fees. Contact: Telegram: @ResQprofirm WhatsApp: +19852969146 Email: [email protected]

  • 13.07.26 23:49 [email protected]

    One of the biggest concerns I have about cryptocurrency is the lack of regulation. It creates opportunities for scammers to invent convincing stories and fraudulent investment schemes. Unfortunately, some social media platforms continue to display these ads because they profit from them, even after users report them.I personally clicked on a Facebook advertisement for a company called Chickenfastmining and ended up losing more than $120,000 in a scam. I reported the ad, but nothing was done. Later, through a Reddit community, I found a recovery service called CYBERBERSPY that, in my personal experience, they helped me recover $110,000 of my lost funds. If you've been a victim of a cryptocurrency scam, don't lose hope. Explore your options carefully, and always verify the legitimacy of any recovery service before trusting them or paying any fees. Based on my own experience, CYBERBERSPY was helpful to me and i was able to recover my funds back, but I encourage everyone to do their own research before using any recovery service.i highly recommend: ([email protected])

  • 15.07.26 11:53 Sarah Green

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  • 15.07.26 11:58 Lily Gagné

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  • 16.07.26 21:38 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. Contact: [email protected]  You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 16.07.26 21:38 patricialovick86

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  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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