Modeling and Optimization of SRAM-based In-Memory Computing Hardware Design

Jyotishman Saikia, Shihui Yin, Sai Kiran Cherupally, Bo Zhang, Jian Meng, Mingoo Seok, Jae Sun Seo

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

Abstract

In-memory computing (IMC) has been demonstrated as a promising technique to significantly improve energy-efficiency for deep neural network (DNN) hardware accelerators. However, designing one involves setting many design variables such as the number of parallel rows to assert, analog-to-digital converter (ADC) at the periphery of memory sub-array, activation/weight precisions of DNNs, etc., which affect energy-efficiency, DNN accuracy, and area. While individual IMC designs have been presented in the literature, they have not investigated this multi-dimensional design optimization. In this paper, to fill this knowledge gap, we present a SRAM-based IMC hardware modeling and optimization framework. A unified systematic study closely models IMC hardware, and investigates how a number of design variables and nonidealities (e.g. device mismatch and ADC quantization) affect the DNN accuracy of IMC design. To maintain high DNN accuracy for the IMC SRAM hardware, it is shown that the number of activated rows, ADC resolution, ADC quantization range, and different sources of variability/noise need to be carefully selected and co-optimized with an underlying DNN algorithm to implement.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages942-947
Number of pages6
ISBN (Electronic)9783981926354
DOIs
StatePublished - Feb 1 2021
Externally publishedYes
Event2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online
Duration: Feb 1 2021Feb 5 2021

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
Volume2021-February
ISSN (Print)1530-1591

Conference

Conference2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
CityVirtual, Online
Period2/1/212/5/21

Keywords

  • deep neural networks
  • In-memory computing
  • modeling
  • optimization
  • SRAM

ASJC Scopus subject areas

  • Engineering(all)

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