2-Bit-Per-Cell RRAM-Based In-Memory Computing for Area-/Energy-Efficient Deep Learning

Wangxin He, Shihui Yin, Yulhwa Kim, Xiaoyu Sun, Jae Joon Kim, Shimeng Yu, Jae Sun Seo

Research output: Contribution to journalArticle

Abstract

In-memory computing (IMC) has emerged as a promising technique for enhancing energy efficiency of deep neural networks (DNNs). While embedded nonvolatile memory, such as resistive RAM (RRAM) is a good alternative to SRAM/DRAM for IMC owing to high density, low leakage, and nondestructive read, most prior works have not demonstrated using multilevel RRAM devices for array-level IMC operations. In this letter, we present an IMC prototype with 2-bit-per-cell RRAM devices for area-/energy-efficient DNN inference. Optimizations on four-level conductance distribution and peripheral circuits with input-splitting scheme have been performed, enabling high DNN accuracy and low area/energy consumption. The prototype chip that monolithically integrated 90-nm CMOS and 2-bit-per- cell RRAM array achieves 87% (83%) CIFAR-10 accuracy and 25 (51) TOPS/W energy efficiency at 1.2 V (0.9 V) supply. At 1.2 V, a stable accuracy of 87% is maintained throughout 108 h.

Original languageEnglish (US)
Article number9145716
Pages (from-to)194-197
Number of pages4
JournalIEEE Solid-State Circuits Letters
Volume3
DOIs
StatePublished - 2020

Keywords

  • Deep neural networks (DNNs)
  • RRAM
  • in-memory computing (IMC)
  • multilevel cell

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of '2-Bit-Per-Cell RRAM-Based In-Memory Computing for Area-/Energy-Efficient Deep Learning'. Together they form a unique fingerprint.

  • Cite this