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TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings

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arxiv 2304.01433 v3 pith:VGAJPDPU submitted 2023-04-04 cs.AR cs.AIcs.LGcs.PF

Norman P. Jouppi , George Kurian , Sheng Li , Peter Ma , Rahul Nagarajan , Lifeng Nai , Nishant Patil , Suvinay Subramanian
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Andy Swing Brian Towles Cliff Young Xiang Zhou Zongwei Zhou David Patterson
This is my paper
classification cs.ARcs.AIcs.LGcs.PF
keywords powerfastermodelslesssupercomputerembeddingsgooglelearning
verification ladder T0 review T1 audit T2 compute T3 formal
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In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes) dynamically reconfigure its interconnect topology to improve scale, availability, utilization, modularity, deployment, security, power, and performance; users can pick a twisted 3D torus topology if desired. Much cheaper, lower power, and faster than Infiniband, OCSes and underlying optical components are <5% of system cost and <3% of system power. Each TPU v4 includes SparseCores, dataflow processors that accelerate models that rely on embeddings by 5x-7x yet use only 5% of die area and power. Deployed since 2020, TPU v4 outperforms TPU v3 by 2.1x and improves performance/Watt by 2.7x. The TPU v4 supercomputer is 4x larger at 4096 chips and thus ~10x faster overall, which along with OCS flexibility helps large language models. For similar sized systems, it is ~4.3x-4.5x faster than the Graphcore IPU Bow and is 1.2x-1.7x faster and uses 1.3x-1.9x less power than the Nvidia A100. TPU v4s inside the energy-optimized warehouse scale computers of Google Cloud use ~3x less energy and produce ~20x less CO2e than contemporary DSAs in a typical on-premise data center.

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