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Аналоговые вычисления для искусственного интеллекта: как делать MAC-операцию при помощи закона Ома

Присутствие нейронных сетей в нашей жизни становится все более распространенным, от голосовых ассистентов до узкоспециальных САПР. Несмотря на то, что область применения нейросетей расширяется, их потенциал все еще сильно ограничен удручающей энергоэффективностью существующих хардверных решений. Специализированные AI-чипы выходят в производство, как горячие пирожки, а ожидаемый рост энергопотребления нагруженных AI датацентров уже скоро позволит им отобрать у атомных электростанций звание главного врага всех экоактивистов. Разумеется, над решением проблемы энергопотребления AI или, будем честны, над решением проблемы энергопотребления цифрового умножения работает огромное количество исследователей по всему миру, на всех возможных уровнях абстракции, от математиков-фундаментальщиков до разработчиков передовых литографов.

А дальше, как это регулярно случается, внезапно оказалось, что все новое — это хорошо забытое старое, и спасение может заключаться в том, чтобы откопать давно забытые за бесперпективностью аналоговые вычисления и взглянуть на них свежим вглядом в свете новых задач.

Как вы себе представляете аналоговый вычислитель

Как аналоговый AI-вычислитель выглядит на самом деле. На картинке — аналоговый чип для решения некоторых простых задач распознавания изображений.

— Нейронные сети "программируются" при помощи варьирования “весов” соединений между нейронами (синапсов). Сигнал, подаваемый на вход системы, проходя через синапс, умножается на его "вес". Как правило, к одному нейрону подходят несколько синапсов, значения их выходов суммируются, и к сумме применяется некоторая нелинейная функция активации. Таким образом, подавляющее большинство вычислительных операций нейронной сети сводится к умножению с накоплением (MAC). Эффективность выполнения MAC-операции является одним из критичных параметров для определения производительности нейронных процессоров.

Структурная диаграмма нейрона, выполняющего умножение с накоплением и активацию

Фундаментально нейронные сети, как и многие другие виды вычислений, могут быть реализованы как на цифровых, так и на аналоговых платформах, но на практике цифровые вычисления уже много десятилетий доминируют, фактически сделав само слово “аналоговый” синонимом чего-то древнего и дремучего. Не в последнюю очередь — потому, что КМОП-технология очень хорошо приспособлена для реализации цифровых схем, а практически неукоснительное выполнение закона Мура на протяжении многих десятилетий сделало ее практически монопольной микроэлектронной технологией. Поэтому практически что угодно делается на КМОП: процессоры, память, драйверы электродвигателей, микросхемы стеклоподъемников, матрицы фотоаппаратов — список такой длинный, что проще перечислить исключения, требующие каких-то выдающихся параметров и одновременно не требующие высокой степени интеграции. А там, где уже есть КМОП, все рано или поздно переходит в цифровой вид, будь то управление обратной связью DC/DC-преобразователя, Software-Defined Radio или цифровой микрофон — практически всегда выгоднее всего поставлять на любой источник аналогового сигнала АЦП, к любому актюатору — драйвер, а между ними все делать в цифровом виде.

Нейросетки, казалось бы, идеально подходят как пример задачи, которую удобно решать в цифровом виде — они огромные, вычислительно сложные и очень сильно выигрывают от миниатюризации и повышения энергоэффективности “железа” выполняющего вычисления. Проблема состоит в том, что цифровое умножение, являющееся ключевой операцией в машинном обучении, крайне ресурсозатратно. Простой 4-битный КМОП-умножитель требует несколько сотен транзисторов. 8-битная схема требует несколько тысяч. Про то, что для работы с floating point необходимо делать FPU, полагаю, не стоит даже и начинать.

Структурная схема цифрового умножителя 4х4 бита. Каждый блок HA (half adder) — это 12-20 транзисторов, каждый блок FA (full adder) — 25-40 транзисторов.

При этом, даже при небольшой точности, “стоимость” умножения усугубляется необходимостью обработки больших объемов данных — входных данных и весов. В современных сложных цифровых системах быстродействие и энергопотребление повсеместно определяются не самыми вычислительными блоками, и передачей данных — так называемым “фон Неймановскими бутылочным горлышком”. Веса, будучи статическими значениями (по крайней мере, во время инференса), тем не менее, требуют удобного доступа, то есть или очень быстрой шины, или локального хранения памяти рядом с вычислительным блоком. Такой подход наиболее эффективен в случае, если у нас не один большой вычислительный блок (скажем, ядро микропроцессора), а много маленьких, каждый со своим собственным хранилищем весов. К сожалению, этот подход плохо работает в случае сложных ветвящихся алгоритмов, являющихся типовой нагрузкой CPU, и именно поэтому современные процессоры в большинстве своем — это RISC-машины с относительно скромным количеством ядер, а какие-нибудь VLIW или “тысячекоры”. Но существует также и широкий класс задач, где ветвлений или совсем нет, или очень мало, а данных много, и их можно обрабатывать параллельно. Например, к этим задачам относится обработка изображений и нейросети. Именно поэтому многие GPU и TPU построены на принципе множества небольших вычислительных ядер с хардверными умножителями и локальной памятью, и именно в силу схожести этих задач выполнять нейросетевые вычисления на GPU радикально эффективнее, чем на CPU. Такого рода системы в контексте AI сейчас называют “near-memory compute”, потому что память весов располагается намного ближе (и физически, и архитектурно) к вычислительным элементам.

Это, впрочем, никак не решает проблему того, что цифровое умножение крайне накладно, и чем больше его надо делать, тем хуже становится ситуация. Огромное количество научных и прикладных работ посвящены тому, как уменьшить разрядность весов и данных в нейросетях без существенной потери точности инференса. Про floating point уже никто толком даже не упоминает, в ход идут int8, int4 или даже “полтора бита” (множество весов, состоящее из -1, 0, +1). Ограниченно-точные сети могут быть полезны не только для огромных LLM, но и для разного рода небольших приложений, вроде умных сенсоров, промышленного интернета вещей и других мест, где можно существенно улучшить энергопотребление, надежность и безопасность за счет локальной обработки чувствительных сырых данных без отправки их на мощные облачные серверы. Действительно же, самый лучший способ защитить свои данные — просто не отправлять их в облако.

Но даже малоразрядные цифровые умножители — это огромное количество транзисторов и на сам умножитель, и на память, ведь даже простая статическая ячейка кэш-памяти — это целых шесть транзисторов. Можно ли как-то перевернуть игру? Теоретически — да, но для этого придется преодолеть скепсис и вернуться к давно забытому старому.

Аналоговый подход к вычислениям считался устаревшим на протяжении десятилетий, и на то были и есть причины. В аналоговых системах существует множество физических и технологических ограничений. Снижение точности и повторяемости результатов вычисления из-за разного рода неидеальностей было одной из причин быстрого роста цифровых технологий и повсеместного отказа от аналога. Да и о какой повторяемости результатов можно говорить, если пороговое напряжение типичного МОП-транзистора может изменяться в пару раз просто из-за изменения температур чипа? На практике в аналоговых вычислительных системах сложно достичь точности, соответствующей более чем четырем цифровым битам (здесь имеется в виду ENOB), а достижение точности выше 8 бит обычно требует радикального усложнения схемотехники и каких-то совсем нетривиальных решений.

Но ведь точность и в восемь, и даже в четыре бита вполне может быть достаточной именно для нейросетей, так? И в этой ситуации аналоговые решения могут предложить то, чего нет у цифры — компактность. Однако аналоговые реализации могут быть очень привлекательными в случаях, когда удовлетворительна точность около 8 бит из-за их простоты и энергоэффективности, и поэтому разработчики микросхем снова рассматривают аналоговые решения. Так как может работать аналоговая MAC?

Ее можно реализовать с помощью… закона Ома. Он гласит, что ток равен напряжению, деленному на сопротивление – или напряжению, умноженному на проводимость резистора. При этом несколько токов можно суммировать, просто подключив весовые резисторы параллельно друг к другу. Итого, для выполнения одного умножения нам нужен один (!) резистор, точность номинала которого и соответствует точности веса в нейросетке — и еще один резистор для того, чтобы получить выходное напряжение из суммы токов. Такой резистор нужен один на целый нейрон (в котором могут быть десятки и сотни весов), так что им для простоты сравнений можно пренебречь.

Но где найти резистор в КМОП-технологии, да еще желательно программируемый? Традиционные поликремниевые или диффузионные резисторы, используемые в аналоговой схемотехнике, вряд ли подойдут: слишком громоздкие. Собственно, единственный тип компактных элементов в КМОП-технологии — это транзисторы. Тут будет кстати вспомнить, что слово “транзистор” является сокращением слова “transresistance”, то есть собственно “переходное сопротивление”. То, что нужно, не правда ли? Осталось только придумать, как управлять сопротивлением этого транзистора, не используя для этого очень много других транзисторов.

Решением этой задачи является NOR flash память. В обычной “цифровой” флэш-памяти у запоминающего транзистора есть два состояния — “открыт” и “закрыт”, регулируемых наличием или отсутствием заряда на плавающем затворе. Но на плавающий затвор можно закладывать заряд по чуть-чуть, плавно регулируя проводимость транзистора. Эта регулировка нелинейная, что затрудняет процесс и снижает точность, но, тем не менее, на ячейке флэш-памяти вполне реально добиться шестнадцати хорошо различимых величин проводимости, то есть эффективной разрядности в четыре бита. И под “вполне реально” я имеют в виду “можно пойти и купить соответствующий IP-блок для ряда популярных техпроцессов нескольких крупных фабрик”.

Структурная схема вычислительного блока in-memory computing на основе популярной в индустрии ячейки NOR Flash ESF3. Модуль можно купить для многих техпроцессов TSMC, Globlafoundries, UMC, Samsung и некоторых других фабрик.

Массив флэш-памяти позволяет использовать строки как входы для данных, а столбцы — как входы нейронов, на которых суммируются токи с резисторов. Массив X на Y ячеек позволяет реализовать один слой нейросети из X нейронов с Y синапсами у каждого нейрона. На рисунке ниже показан пример такого соединения эквивалентных сопротивлений (еще показаны некоторые паразитные элементы). Каждый выходной ток I_i является взвешенной суммой напряжений V₁-V_n, а проводимости элементов G_ij — веса нейросети.

Принципиальная структура массива памяти, используемая для создания "вычислений в памяти"

MAC-операция в такой конструкции, как нетрудно заметить, вычисляется непосредственно внутри массива памяти, и такого рода схемы так и называют — “in-memory computing”, и рассматривают как следующий логичный шаг после перехода от фон Неймановских архитектур с разделением памяти и вычислений к near-memory, характерным для GPU.

А вот примерно так распознавание изображений с помощью аналогового in-memory computing выглядит на практике. На это диаграмме в качестве запоминающих элементов показаны мемристоры.

Подход in-memory computing может быть реализован многими разными способами (об этом чуть позже), но вариант со флэш-памятью — самый инженерно проработанный. Он имеет два недостатка. Ячейки флэш-памяти, хоть и состоят из одного транзистора, по меркам современных цифровых техпроцессов довольно громоздки, и, вообще говоря, практически не скейлятся ниже 45 нм. Кроме того, любой массив памяти содержит соединения типа “все со всеми”, но далеко не каждая нейронная сеть нуждается в таком количестве синапсов. Более того, очень большая часть нейросетей — это так называемые “разреженные” (sparse) сети. В них большая часть ячеек памяти не будет использоваться и дефицитная площадь чипа не будет потрачена эффективно. Разумеется, можно и нужно оптимизировать архитектуры нейросетей для того, чтобы уменьшить разреженность и увеличить процент эффективного использования массива. Но даже в такой ситуации половина площади, а то и больше, будет потрачена впустую.

Хороший пример реализации такого подхода — чипы компании Mythic AI. Их M1076 включает 76 вычислительный блоков, каждый из которых содержит массив 1024х1024 ячеек памяти и RISC-V ядро для управления и конфигурации. Чип может вместить до 80 миллионов int8 или int4 весов и, по заверению разработчиков, потребляет всего 3-4 Вт в активном режиме. Заявленная энергоэффективность — 0.3-0.5 пДж/MAC, что в 3-4 раза лучше, чем у NVIDIA (при том, что чип Mythic произведен на дешевом 40 нм техпроцессе).

Блок-схема аналогового нейропроцессора компании Mythic AI

Разумеется, такая конструкция на таких проектных нормах получается совершенно монструозного размера, поэтому одной из ключевых задач ученых, вовлеченных в касающиеся AI технологические исследования, является разработка компактных ячеек энергонезависимой памяти — более компактных, чем NOR flash и, в идеале, не содержащих транзисторов, а выполненных в верхних слоях металлизации, чтобы схемы доступа управления можно было разместить под памятью.

Вариантов здесь очень много — MRAM, FRAM, PCM, ReRAM, мемристоры — и для каждого вида есть множество перспективных вариантов. Или уже не очень перспективных, как случилось с не оправдавшей ожиданий Intel и Micron памятью 3D Xpoint.

Дерево технологий emerging NVM

Несмотря на множество сложностей, так называемые emerging NVM активно развиваются, и MRAM уже есть в серийном предложении нескольких фабрик и скажем, в новейших моделях микроконтроллеров STM32. Однако, с точки зрения аналоговой реализации MAC-операции, все несколько более печально, потому что большинство новых разработок в сфере NVM работает бинарно, что вполне достаточно для конфигурационной памяти микроконтроллера, но не подходит для многобитного аналогового веса. Впрочем, некоторый прогресс есть и тут: недавно многобитные мемристоры представила японская корпорация TDK, а американский стартап TetraMem вообще обещает точность 11 бит на элемент, достигаемую с помощью хитрой двухступенчатой процедуры записи.

Забавная особенность решительно всех видов emerging NVM — то, что состояния запоминающего элемента различаются сопротивлением. С одной стороны, это логично, ведь резистор — это один из фундаментальных линейных пассивных компонентов. Но ведь “один из”, а не единственный!

Фундаментальные линейные пассивные элементы и связывающие их уравнения

На рисунке выше показаны все четыре фундаментальных линейных пассивных компонента — резистор, конденсатор, индуктивность и мемристор, а также уравнения, которыми эти элементы связывают разные физические величины. Нетрудно видеть, что рисунок очень симметричный, и если MAC-операцию можно реализовать при помощи резистора, то и с остальными компонентами это может быть возможно, так? Да, так, но, как это обычно бывает, кроме красивой теории, есть некоторые технологические нюансы.

Уравнение работы резистора — I = V / R, или, точнее, dI = dV / R.

Аналогичное уравнение для конденсатора — Q = T * I, или dQ = C * dV.

Если мы хотим получить в качестве выходного сигнала напряжение на конденсаторе, мы уравнение преобразуется в dV = d(I * T) / C. Принципиальная разница между резистором и конденсатором здесь заключается в том, что в уравнении для конденсатора участвуют не три, а четыре величины. Это означает, что у проектировщика схемы есть больше гибкости, но и сложностей тоже больше.

Существует много разных вариантов запоминающего элемента на основе конденсатора. Выходной величиной удобнее всего иметь напряжение или заряд, а вот входом может быть либо ток, либо время, и тогда вес будет произвольной комбинацией двух оставшихся параметров. Например, схема может получать выходное напряжение, заряжая фиксированный конденсатор в течение фиксированного времени с помощью тока, определенного весом. Альтернативно, выходное напряжение может быть получено при зарядке конденсатора, номинал которого является весом, в течение фиксированного времени, а входным сигналом будет ток зарядки.

Реализация MAC-операции с весом, задаваемым временем зарядки конденсатора

На рисунке выше показана схема, где конденсатор фиксирован, а вес определяется временем зарядки. С одной стороны, MAC-операция в такой схеме проводится на одном конденсаторе. С другой стороны, схема точного задания временного интервала — это громоздкий таймер, работающий на большой частоте, требующий наличия на чипе PLL. Кроме того, вся конструкция вполне может быть серьезно зависима от PVT (изменений в параметрах элементов, температуры и питания чипа). Выглядит, к сожалению, и громоздко, и не очень энергоэффективно.

Можно попробовать перейти в область работы с зарядами вместо напряжений, как это часто делается в популярных у аналоговых дизайнеров схемах на переключаемых конденсаторах. В этом случае основное уравнение будет Q = C * V, а суммирование заряда получится относительно простым.

Реализация MAC-операции с помощью ЦАП

На рисунке выше для реализации веса нейросети используется ЦАП (цифро-аналоговый преобразователь) с емкостной матрицей. Значение емкости определяется цифровым кодом, что является наиболее подходящим способом программирования конденсатора. Обычно у ЦАП фиксированное опорное напряжение, и он генерирует выходной сигнал в соответствии с цифровым входом. Здесь же цифровой вход трактуется как вес, а вместо опорного напряжения мы подаем входное, умножая его на заданный конденсатором вес. Такой подход исключает точное время и высокие частоты, делая выходной сигнал стабильным и точным. Однако даже очень простой ЦАП представляет собой сложную и крупную схему, площадью в сотни, если не тысячи квадратных микрон. Решением этой проблемы мог бы быть компактный переменный конденсатор (memcapacitor). Научные работы в этом направлении активно ведутся в последние годы, но пока что далеки от промышленного внедрения и находятся в среднем на более ранних стадиях, чем аналогичные работы по мемристорам.

К вопросу о собственно мемристорах — их тоже можно использовать, и тоже несколькими разными способами. Мемристор был теоретически предсказан намного позже, чем получили распространение другие полупроводниковые приборы, а надежные реализации и вовсе появились совсем недавно. Основное уравнение мемристора связывает электрический заряд, протекший через мемристор, с суммарным магнитным потоком. Номинал мемристора — мемсопротивление — имеет размерность Вебер/Кулон, которые после сокращения дают… Ом. То есть, мемристор, будучи фундаментально другим элементом, может вести себя, как резистор, сопротивление которого зависит от протекшего через него заряда. Отсуда, собственно, и название — “резистор с памятью”, и основное применение, которое заключается в том, что мемристоры используют к программируемые резисторы. Если рабочий ток намного меньше тока перезаписи, то влиянием рабочего тока на сопротивление можно пренебречь на значительных промежутках времени, и мемристор будет действительно вести себя как программируемый сильными токовыми импульсами резистор, очень похоже на многие другие типы emerging NVM.

Пример компактной реализации мемристора в форм-факторе перемычки между двумя слоями металических межсоединений

Основная причина, по которой мемристорам уделяется так много внимания — их потенциальная компактность. Существует уже много разных реализаций мемристора в виде многослойного вертикального бутерброда из разных материалов. Такая реализация не тратит драгоценную транзисторную площадь и теоретически может иметь размер в буквально несколько сотен квадратных нанометров. То же самое, впрочем, верно и для MRAM и ReRAM.

Последний из четырех базовых линейных элементов — индуктивность. С ней все так сложно и технологически, и фундаментально, что катушки никто серьезно не рассматривает для микросхем с большой степенью интеграции. Во-первых, индуктивность в принципе требует высокой частоты для того, чтобы нормально работать, и поэтому не может применяться в малопотребляющих схемах. Во-вторых, чем меньше геометрические размеры катушки, чем выше необходимая частота. Ну и самое главное — характерные размеры катушек индуктивности в принципе на пару порядков больше, чем то, что можно серьезно рассматривать. Если вы когда-нибудь видели кристалл любого радиочастотного чипа, то вы точно видели там катушки на полкристалла.

Радиочастотный чип VCO. Размеры катушек индуктивности говорят сами за себя

Заключение

Мы рассмотрели различные теоретические и практические варианты реализации операции умножения-накопления в аналоговой форме на основе разных полупроводниковых приборов. Это направление разработки “железа” для AI находится в весьма зачаточной стадии, особенно если сравнивать с хорошо проработанными цифровыми реализациями — не только на видеокартах, сейчас уже даже в микроконтроллерах начали появляться небольшие встроенные нейроускорители.

Вероятнее всего, аналоговые реализации AI, даже если “выстрелят”, так или иначе останутся нишевыми решениями — в силу принципиально ограниченной на уровне 4-8 бит точности, сложных производственных технологий и многих других объективных причин. Но в условиях, когда AI применяется приблизительно везде, даже “отдельные ниши” вполне могут быть многомиллиардными рынками, особенно если мы говорим про обработку изображений или копеечные, но исключительно многотиражные умные сенсоры для интернета вещей.

Кроме того, технический и технологический прогресс не стоят на месте, так что элементная база для подобных вычислений стремительно улучшается, а число стартапов, пытающихся в отдельных нишах коммерциализировать уже существующие наработки, прямо сегодня исчисляется десятками, если даже не сотнями. Наибольшие успехи сейчас у in-memory computing на базе флэш-памяти, и у разного рода нишевых решений, в основном касающихся умных сенсоров и производимых на довольно старых по цифровым меркам проектных нормах — 90-65-40 нм, где сходятся между собой низкая стоимость и высокая энергоэффективность.

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19 июн. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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27.08.24 12:50 James889900

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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

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27.11.24 00:39 [email protected]

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.