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Device Characteristic-Aware Quantization for eFlash-Based In-Memory Computing SoC

  • Yizhe Chen*
  • , Guangyao Wang
  • , Hanjie Liu
  • , Yuexi Lv
  • , Yuannuo Feng
  • , Minghua Tang
  • , Yong Pei
  • , Wang Kang
  • *Corresponding author for this work
  • Beihang University
  • XiangTan University
  • Zhicun Research Lab

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

Abstract

This study presents an eFlash-based in-memory computing (IMC) SoC leveraging device characteristic-aware quantization to enhance accuracy. A non-uniform quantization method, based on the non-linear characteristics of eFlash devices, is proposed to simplify the control of the number of electrons. Additionally, several channel-wise quantization schemes are introduced, considering the characteristics of the array and data distribution. These methods significantly improve image classification accuracy on the CIFAR-10 dataset using a CNN network, achieving enhancements of f2. 2%, 4. 2%, 5. 6%, and f1 4. 4% compared to baseline models.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-71
Number of pages2
ISBN (Electronic)9798331530709
DOIs
StatePublished - 2024
Event2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024 - Hangzhou, China
Duration: 25 Oct 202427 Oct 2024

Publication series

Name2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024

Conference

Conference2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024
Country/TerritoryChina
CityHangzhou
Period25/10/2427/10/24

Keywords

  • accuracy enhancement
  • eFlash
  • in-memory computing
  • quantization

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