Title

UPTPU: Improving Energy Efficiency of a Tensor Processing Unit through Underutilization Based Power-Gating

Document Type

Conference Paper

Journal/Book Title/Conference

Proceedings - Design Automation Conference

Publisher

Institute of Electrical and Electronics Engineers

Publication Date

12-5-2021

Funder

National Science Foundation

First Page

325

Last Page

330

Abstract

The AI boom is bringing a plethora of domain-specific architectures for Neural Network computations. Google's Tensor Processing Unit (TPU), a Deep Neural Network (DNN) accelerator, has replaced the CPUs/GPUs in its data centers, claiming more than 15 × rate of inference. However, the unprecedented growth in DNN workloads with the widespread use of AI services projects an increasing energy consumption of TPU based data centers. In this work, we parametrize the extreme hardware underutilization in TPU systolic array and propose UPTPU: an intelligent, dataflow adaptive power-gating paradigm to provide a staggering 3.5 ×-6.5× energy efficiency to TPU for different input batch sizes.

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