DPe-CIM: A 4T1C Dual-Port eDRAM Compute-in-Memory for Simultaneous Computing and Refresh with Adaptive Refresh and Data Conversion Reduction Scheme

Dohan Kim, Minyoung Jo, Gi Seok Kim, Dong Gyun Ha, Ung Bin Oh, Seong Ook Jung

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

Abstract

Recently, eDRAM-based computing-in-memory operating in analog domain (ACIM) has been proposed to enhance area and energy efficiency of AI computations [1-5]. However, conventional ACIMs have three challenges. First, previous structures [1-3] have primarily focused on computing area or energy efficiency without considering refresh overhead. This may not be a problem in small DNN models. However, as shown in Fig. 1, refresh overhead has become critical with the increase in DNN model size for higher accuracy and the decrease in data retention time (DRT) due to technology scaling. Second, a pre-measured refresh period at the worst corner used in [4, 5] causes unnecessary energy consumption in most PVT corners, as DRT in eDRAM significantly varies depending on the PVT corner. The last challenge is the significant area and energy overhead of data conversion circuits (DAC or ADC) required in ACIMs to ensure DNN accuracy [4-8].

Original languageEnglish
Title of host publication2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350376326
DOIs
Publication statusPublished - 2024
Event2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024 - Hiroshima, Japan
Duration: 2024 Nov 182024 Nov 21

Publication series

Name2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024

Conference

Conference2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024
Country/TerritoryJapan
CityHiroshima
Period24/11/1824/11/21

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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