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In-Datacenter Performance Analysis of a Tensor Processing Unit

Norman P. Jouppi
Cliff Young
Nishant Patil
Gaurav Agrawal
Raminder Bajwa
Sarah Bates
Suresh Bhatia
Nan Boden
Al Borchers
Rick Boyle
Pierre-luc Cantin
Clifford Chao
Chris Clark
Jeremy Coriell
Mike Daley
Matt Dau
Ben Gelb
Tara Vazir Ghaemmaghami
Rajendra Gottipati
William Gulland
Robert Hagmann
C. Richard Ho
Doug Hogberg
John Hu
Dan Hurt
Julian Ibarz
Aaron Jaffey
Alek Jaworski
Alexander Kaplan
Harshit Khaitan
Andy Koch
Naveen Kumar
Steve Lacy
James Law
Diemthu Le
Chris Leary
Zhuyuan Liu
Kyle Lucke
Alan Lundin
Gordon MacKean
Adriana Maggiore
Maire Mahony
Kieran Miller
Rahul Nagarajan
Ravi Narayanaswami
Ray Ni
Kathy Nix
Thomas Norrie
Mark Omernick
Narayana Penukonda
Andy Phelps
Jonathan Ross
ISCA (2017) (to appear)

Abstract

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.