KARS: Kernel-Grouping Aided Row-Skipping for SDK-based Weight Compression in PIM Arrays

Juhong Park, Johnny Rhe, Jong Hwan Ko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With energy-efficient computation, processing-in-memory (PIM) architectures have been highlighted as one of the most viable candidates to substitute the traditional ones. Recently, shift and duplicate kernel (SDK) mapping method was proposed to enable efficient and fast convolutional neural networks (CNNs) inference in the PIM array. However, since its weight deployment to reuse input data, this method generates idle cells that do not involved in the computation, which leads to an increase of energy consumption. In this paper, we propose a novel weight mapping method called kernel-grouping aided row-skipping (KARS). KARS maximizes utilization by removing idle cells on a PIM array and reduces computing cycles. In comparison to the traditional methods, KARS achieves a speedup by up to 3× at Layer 2 of VGGNet-13 and ResNet-18.

Original languageEnglish
Title of host publicationISCAS 2024 - IEEE International Symposium on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350330991
DOIs
StatePublished - 2024
Event2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024 - Singapore, Singapore
Duration: 19 May 202422 May 2024

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

Conference2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024
Country/TerritorySingapore
CitySingapore
Period19/05/2422/05/24

Keywords

  • convolutional neural network (CNN)
  • processing-in-memory (PIM)
  • weight mapping

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