Vesti: An In-Memory Computing Processor for Deep Neural Networks Acceleration

Zhewei Jiang, Shihui Yin, Minkyu Kim, Tushar Gupta, Mingoo Seok, Jae Sun Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present Vesti, a Deep Neural Network (DNN) accelerator optimized for energy-constrained hardware platforms such as mobile, wearable, and Internet of Things (IoT) devices. Vesti integrates instances of in-memory computing (IMC) SRAM macros with an ensemble of peripheral digital circuits for dataflow management. The IMC SRAM macros eliminate the data access bottleneck that hinders conventional ASIC implementations performing dot-product computation, while the peripheral circuits improve the macros' parallelism and utilization for practical applications. Vesti supports large-scale DNNs with configurable activation precision, substantially improving chip-level energy-efficiency with favorable accuracy trade-off. The Vesti accelerator is designed and laid out in 65 nm CMOS, demonstrating ultra-low energy consumption of less than <20nJ for MNIST classification and <40μJ for CIFAR-10 classification at 1.0V supply.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1516-1521
Number of pages6
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period11/3/1911/6/19

Keywords

  • In-memory computing
  • SRAM
  • deep learning accelerator
  • deep neural networks
  • double-buffering

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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