{"id":6574,"date":"2025-11-04T11:12:00","date_gmt":"2025-11-04T11:12:00","guid":{"rendered":"https:\/\/lp.szlogic.cn\/glossary\/tpu-tensor-processing-unit-google-ai-accelerator\/"},"modified":"2026-06-22T05:34:25","modified_gmt":"2026-06-22T05:34:25","slug":"tpu-tensor-processing-unit-google-ai-accelerator","status":"publish","type":"post","link":"https:\/\/resourceslp.szlogic.cn\/pt\/glossary\/tpu-tensor-processing-unit-google-ai-accelerator","title":{"rendered":"Entendendo a TPU: dentro da arquitetura da Tensor Processing Unit do Google"},"content":{"rendered":"<figure class=\"wp-block-image aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1200\" height=\"712\" src=\"https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/54d67b7b0d92483599dd22af221ec259.webp\" alt=\"What Is TPU?\" class=\"wp-image-6570\" srcset=\"https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/54d67b7b0d92483599dd22af221ec259.webp 1200w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/54d67b7b0d92483599dd22af221ec259-300x178.webp 300w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/54d67b7b0d92483599dd22af221ec259-1024x608.webp 1024w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/54d67b7b0d92483599dd22af221ec259-768x456.webp 768w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/54d67b7b0d92483599dd22af221ec259-18x12.webp 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f O que \u00e9 uma TPU (Unidade de Processamento de Tensores)?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A <strong>Unidade de Processamento de Tensores (TPU)<\/strong> \u00e9 um acelerador de IA personalizado desenvolvido pelo Google para acelerar cargas de trabalho de aprendizado de m\u00e1quina \u2014 especialmente opera\u00e7\u00f5es de aprendizado profundo baseadas em grandes computa\u00e7\u00f5es de tensores e matrizes. Ao contr\u00e1rio de CPUs ou GPUs, as TPUs s\u00e3o especializadas <a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/glossary\/what-is-application-specific-integrated-circuit-asic\/\">ASICs<\/a> projetadas para treinamento e infer\u00eancia de redes neurais com alto rendimento e alta efici\u00eancia em escala.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Por que o Google criou a TPU<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" >Otimizada para Aprendizado Profundo<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Redes neurais exigem opera\u00e7\u00f5es matem\u00e1ticas massivas em paralelo, principalmente tarefas de multiplica\u00e7\u00e3o e acumula\u00e7\u00e3o de matrizes. <a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/glossary\/what-is-cpu-central-processing-unit\/\"><strong>CPUs<\/strong><\/a> t\u00eam dificuldade com essas cargas de trabalho, enquanto <a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/glossary\/what-is-a-gpu-graphics-processing-units\/\"><strong>GPUs<\/strong><\/a>, embora potentes, s\u00e3o aceleradores de prop\u00f3sito geral.<br\/><strong>TPUs <\/strong>foram criadas para:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>Oferecer desempenho extremamente alto por watt<\/p><\/li><li><p>Maximizar a taxa de transfer\u00eancia de multiplica\u00e7\u00f5es de matrizes<\/p><\/li><li><p>Suportar modelos de IA em larga escala de forma economicamente vi\u00e1vel<\/p><\/li><li><p>Atender \u00e0 crescente demanda interna do Google em Busca, Tradutor, YouTube, Maps e modelos de IA<\/p><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" >Projeto voltado para IA<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Desde o in\u00edcio, a <strong>arquitetura da TPU<\/strong> concentrou-se em:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>Co-projeto hardware-software com TensorFlow<\/p><\/li><li><p>Formatos de precis\u00e3o reduzida (por exemplo, bfloat16, int8) para computa\u00e7\u00e3o energeticamente eficiente<\/p><\/li><li><p>F\u00e1bricas escal\u00e1veis para agrupamento multi-chip<\/p><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Arquitetura da TPU explicada<\/h2>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/d1ac50e745d64fcb9d389c7931db629e.png\" alt=\"TPU Architecture\" class=\"wp-image-6571\" srcset=\"https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/d1ac50e745d64fcb9d389c7931db629e.png 1536w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/d1ac50e745d64fcb9d389c7931db629e-300x200.png 300w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/d1ac50e745d64fcb9d389c7931db629e-1024x683.png 1024w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/d1ac50e745d64fcb9d389c7931db629e-768x512.png 768w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/d1ac50e745d64fcb9d389c7931db629e-18x12.png 18w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" >Motores Matriciais Sist\u00f3licos<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No n\u00facleo de cada chip TPU encontra-se uma <strong>unidade massiva de multiplica\u00e7\u00e3o de matrizes<\/strong> organizada em uma matriz sist\u00f3lica, permitindo milhares de opera\u00e7\u00f5es simult\u00e2neas de multiplica\u00e7\u00e3o e acumula\u00e7\u00e3o.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" >Mem\u00f3ria de Alta Largura de Banda<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As TPUs modernas integram <strong>HBM<\/strong> para fornecer dados com largura de banda extremamente alta, evitando gargalos de mem\u00f3ria comuns em sistemas baseados em GPU.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" >Interconex\u00e3o e Escalabilidade<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">TPUs individuais escalonam para <strong>Pods TPU<\/strong>, interconectados por redes de baixa lat\u00eancia e alta largura de banda para clusters modulares de IA multi-exaflop.<br\/>Essa arquitetura permite o treinamento de modelos extremamente grandes e infer\u00eancia mais r\u00e1pida em escala hipermassiva.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Gera\u00e7\u00f5es de TPU e principais especifica\u00e7\u00f5es<\/h2>\n\n\n\n<figure class=\"wp-block-table\">\n<table class=\"has-fixed-layout\">\n<colgroup><col style=\"width: 134px;\"\/><col style=\"width: 200px;\"\/><col style=\"width: 179px;\"\/><col style=\"min-width: 25px;\"\/><\/colgroup><tbody><tr><th colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Gera\u00e7\u00e3o<\/p><\/th><th colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Foco<\/p><\/th><th colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>Mem\u00f3ria e Computa\u00e7\u00e3o<\/p><\/th><th colspan=\"1\" rowspan=\"1\"><p>SFP (definido pelo MSA)<\/p><\/th><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>TPU v1<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Infer\u00eancia<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>Computa\u00e7\u00e3o de 8 bits<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Primeira implanta\u00e7\u00e3o interna<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>TPU v2<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Treinamento e Infer\u00eancia<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>bfloat16, HBM<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Lan\u00e7amento da Cloud TPU<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>TPU v3<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Treinamento em larga escala<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>Refrigera\u00e7\u00e3o l\u00edquida, HBM<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Pod com at\u00e9 ~1.000 chips<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>TPU v4<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Pods eficientes de exaescala<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>HBM de 32 GB, malha avan\u00e7ada<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Escala de data center<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>TPU v6 \u201cTrillium\u201d<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Computa\u00e7\u00e3o de IA de alta densidade<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>V\u00e1rias pilhas de HBM<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Desempenho ~5\u00d7 superior ao anterior<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>TPU v7 \u201cIronwood\u201d<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"200\"><p>Arquitetura voltada prioritariamente para infer\u00eancia<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"179\"><p>Otimiza\u00e7\u00e3o FP8<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Projetada para atendimento de LLMs<\/p><\/td><\/tr><\/tbody>\n<\/table>\n<\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f TPU vs GPU vs CPU<\/h2>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" width=\"1200\" height=\"315\" src=\"https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/a83e15692e184550860b10ac91d93a99.webp\" alt=\"TPU vs GPU vs CPU\" class=\"wp-image-6572\" srcset=\"https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/a83e15692e184550860b10ac91d93a99.webp 1200w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/a83e15692e184550860b10ac91d93a99-300x79.webp 300w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/a83e15692e184550860b10ac91d93a99-1024x269.webp 1024w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/a83e15692e184550860b10ac91d93a99-768x202.webp 768w, https:\/\/resourceslp.szlogic.cn\/wp-content\/uploads\/2026\/05\/a83e15692e184550860b10ac91d93a99-18x5.webp 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-table\">\n<table class=\"has-fixed-layout\">\n<colgroup><col style=\"width: 134px;\"\/><col style=\"width: 194px;\"\/><col style=\"min-width: 25px;\"\/><col style=\"min-width: 25px;\"\/><\/colgroup><tbody><tr><th colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Recurso<\/p><\/th><th colspan=\"1\" rowspan=\"1\" colwidth=\"194\"><p>TPU<\/p><\/th><th colspan=\"1\" rowspan=\"1\"><p><a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/glossary\/what-is-a-gpu-graphics-processing-units\/\">GPU<\/a><\/p><\/th><th colspan=\"1\" rowspan=\"1\"><p><a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/glossary\/what-is-cpu-central-processing-unit\/\">CPU<\/a><\/p><\/th><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Prop\u00f3sito<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"194\"><p>Computa\u00e7\u00e3o tensorial espec\u00edfica para IA<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Acelera\u00e7\u00e3o gr\u00e1fica e de ML<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Computa\u00e7\u00e3o geral<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Melhor Para<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"194\"><p>Redes neurais, LLMs<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>HPC, ML, gr\u00e1ficos<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>SO, l\u00f3gica, aplicativos<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Paralelismo<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"194\"><p>Extremamente alto<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>High<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Baixa<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Efici\u00eancia<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"194\"><p>M\u00e1ximo para cargas de trabalho de IA<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>High<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Uso geral<\/p><\/td><\/tr><tr><td colspan=\"1\" rowspan=\"1\" colwidth=\"134\"><p>Implanta\u00e7\u00e3o<\/p><\/td><td colspan=\"1\" rowspan=\"1\" colwidth=\"194\"><p>Nuvem e clusters<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Nuvem e local (on-prem)<\/p><\/td><td colspan=\"1\" rowspan=\"1\"><p>Em toda parte<\/p><\/td><\/tr><\/tbody>\n<\/table>\n<\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Em resumo:<\/strong><\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p><em>CPUs s\u00e3o universais. GPUs s\u00e3o vers\u00e1teis. TPUs s\u00e3o extremamente focadas em IA em escala.<\/em><\/p><\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Onde as TPUs s\u00e3o utilizadas<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" >Treinamento de modelos em larga escala<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Ideal para modelos transformadores, sistemas de recomenda\u00e7\u00e3o e pipelines de treinamento de grandes modelos de linguagem.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" >Infer\u00eancia na nuvem<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As TPUs impulsionam servi\u00e7os globais <a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/knowledge-center\/link-pp-optical-modules-ai-iot-big-data-performance-reliability\/\">cargas de trabalho de IA<\/a> como classifica\u00e7\u00e3o de buscas, tradu\u00e7\u00e3o de idiomas, reconhecimento de fala e servi\u00e7os de IA generativa.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" >Edge TPU<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Uma variante leve de TPU executa infer\u00eancia de ML localmente em dispositivos de borda\/embutidos para IA com baixa lat\u00eancia e efici\u00eancia energ\u00e9tica <a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/knowledge-center\/iot-internet-of-things-definition-and-real-world-examples\/\">IoT<\/a> intelig\u00eancia.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Melhores pr\u00e1ticas para implanta\u00e7\u00e3o de TPUs<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>Utilize tipos de dados suportados (bfloat16 \/ int8) para m\u00e1xima efici\u00eancia<\/p><\/li><li><p>Otimize os pipelines de dados para computa\u00e7\u00e3o distribu\u00edda<\/p><\/li><li><p>Escolha TPU Pods para cargas de trabalho em escala de LLM<\/p><\/li><li><p>Considere o projeto t\u00e9rmico e de rede para escalabilidade do cluster<\/p><\/li><li><p>Aproveite estrat\u00e9gias h\u00edbridas de nuvem + borda para densidade equilibrada de computa\u00e7\u00e3o<\/p><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f TPUs e o futuro da infraestrutura de IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Os modelos de IA s\u00e3o mais intensivos em computa\u00e7\u00e3o do que nunca, deslocando o foco do treinamento puro para <strong>infer\u00eancia em tempo real em escala<\/strong>.<br\/>As TPUs continuar\u00e3o avan\u00e7ando em:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>Densidade de interconex\u00e3o<\/p><\/li><li><p>Arquiteturas energeticamente eficientes<\/p><\/li><li><p>Precis\u00e3o h\u00edbrida (por exemplo, FP8)<\/p><\/li><li><p>Integra\u00e7\u00e3o com frameworks de software (TensorFlow, JAX, PyTorch via XLA)<\/p><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">\u00c0 medida que as cargas de trabalho de IA aceleram, computa\u00e7\u00e3o especializada e conectividade ultrarr\u00e1pida tornam-se componentes essenciais da <a target=\"_blank\" rel=\"\" href=\"https:\/\/resourceslp.szlogic.cn\/pt\/knowledge-center\/what-is-a-data-center\/\">moderna infraestrutura de data center<\/a> e projeto de rede.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Como isso se relaciona com LINK-PP<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A acelera\u00e7\u00e3o de IA em hiperaescala depende de redes avan\u00e7adas e infraestrutura robusta de conectividade. <a target=\"_blank\" rel=\"\" href=\"https:\/\/www.l-p.com\/\">LINK-PP<\/a> componentes suportam o ambiente de data center que alimenta implanta\u00e7\u00f5es de TPUs, incluindo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>Solu\u00e7\u00f5es de rede de alta velocidade, <a target=\"_blank\" rel=\"\" href=\"https:\/\/www.l-p.com\/store-17492-integrated-rj45-connector.htm\"><strong>MagJacks RJ45<\/strong><\/a><\/p><\/li><li><p><strong>SFP\/25G\/100G<\/strong> <a target=\"_blank\" rel=\"\" href=\"https:\/\/www.l-p.com\/store-25432-optics-transceivers-sfp-modules.htm\">m\u00f3dulos \u00f3pticos<\/a><\/p><\/li><li><p><strong>PoE<\/strong> solu\u00e7\u00f5es para dispositivos de IA de borda<\/p><\/li><li><p>Conectores Ethernet industrial e IoT<\/p><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" >\u2699\ufe0f Conclus\u00e3o<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>TPUs<\/strong> representam um grande avan\u00e7o em computa\u00e7\u00e3o especializada <strong>de IA<\/strong>\u2014projetados especificamente para cargas de trabalho de tensores e opera\u00e7\u00f5es em larga escala de redes neurais. \u00c0 medida que a ado\u00e7\u00e3o global de IA generativa e aprendizado profundo acelera, as TPUs desempenham um papel crucial no fornecimento de energia para clusters de treinamento e infraestrutura de infer\u00eancia.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Para ind\u00fastrias que constroem ou apoiam ambientes modernos de data center, compreender a tecnologia TPU fornece insights valiosos sobre as exig\u00eancias de sistemas de IA de alto desempenho\u2014e sobre as oportunidades em hardware e componentes de rede de pr\u00f3xima gera\u00e7\u00e3o.<\/p>","protected":false},"excerpt":{"rendered":"<p>Saiba o que \u00e9 uma TPU (Tensor Processing Unit), como funciona o acelerador de IA do Google, as principais gera\u00e7\u00f5es de TPU, compara\u00e7\u00e3o entre TPU e GPU e seu papel no aprendizado de m\u00e1quina em larga escala e eficiente.<\/p>","protected":false},"author":1,"featured_media":6573,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[27],"tags":[22,24,26],"class_list":["post-6574","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-glossary","tag-integrated-rj45-connectors","tag-link-pp","tag-optics-transceivers"],"blocksy_meta":[],"acf":[],"_links":{"self":[{"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/posts\/6574","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/comments?post=6574"}],"version-history":[{"count":5,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/posts\/6574\/revisions"}],"predecessor-version":[{"id":10935,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/posts\/6574\/revisions\/10935"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/media\/6573"}],"wp:attachment":[{"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/media?parent=6574"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/categories?post=6574"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/resourceslp.szlogic.cn\/pt\/wp-json\/wp\/v2\/tags?post=6574"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}