Improving DNN Hardware Accuracy by In-Memory Computing Noise Injection

Sai Kiran Cherupally, Jian Meng, Adnan Siraj Rakin, Shihui Yin, Mingoo Seok, Deliang Fan, Jae Sun Seo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Editor's notes: Like any other designs, in-memory computing (IMC) also suffers from operational inaccuracy induced by hardware noise. In this work, the authors propose to take into account hardware noises during the deep neural network (DNN) training in order to improve the DNN inference accuracy.-Yiran

Original languageEnglish (US)
Pages (from-to)71-80
Number of pages10
JournalIEEE Design and Test
Issue number4
StatePublished - Aug 1 2022


  • Deep neural network
  • Hardware-aware training
  • In-memory computing
  • Noise injection

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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