CMP-PIM: An energy-efficient comparator-based processing-in-memory neural network accelerator

Shaahin Angiziy, Zhezhi Hey, Adnan Siraj Rakin, Deliang Fan

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

20 Scopus citations

Abstract

In this paper, an energy-efficient and high-speed comparator-based processing-in-memory accelerator (CMP-PIM) is proposed to efficiently execute a novel hardware-oriented comparator-based deep neural network called CMPNET. Inspired by local binary pattern feature extraction method combined with depthwise separable convolution, we first modify the existing Convolutional Neural Network (CNN) algorithm by replacing the computationally-intensive multiplications in convolution layers with more efficient and less complex comparison and addition. Then, we propose a CMP-PIM that employs parallel computational memory sub-array as a fundamental processing unit based on SOT-MRAM. We compare CMP-PIM accelerator performance on different data-sets with recent CNN accelerator designs. With the close inference accuracy on SVHN data-set, CMP-PIM can get ∼ 94× and 3× better energy efficiency compared to CNN and Local Binary CNN (LBCNN), respectively. Besides, it achieves 4.3× speed-up compared to CNN-baseline with identical network configuration.

Original languageEnglish (US)
Title of host publicationProceedings of the 55th Annual Design Automation Conference, DAC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781450357005
DOIs
StatePublished - Jun 24 2018
Externally publishedYes
Event55th Annual Design Automation Conference, DAC 2018 - San Francisco, United States
Duration: Jun 24 2018Jun 29 2018

Publication series

NameProceedings - Design Automation Conference
VolumePart F137710
ISSN (Print)0738-100X

Other

Other55th Annual Design Automation Conference, DAC 2018
CountryUnited States
CitySan Francisco
Period6/24/186/29/18

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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