Skew-CIM: Process-Variation-Resilient and Energy-Efficient Computation-in-Memory Design Technique With Skewed Weights

Donghyeon Yi, Seoyoung Lee, Injun Choi, Gichan Yun, Edward Jongyoon Choi, Jonghee Park, Jonghoon Kwak, Sung Joon Jang, Sohmyung Ha, Ik Joon Chang, Minkyu Je

Research output: Contribution to journalArticlepeer-review


In analog-mixed-signal (AMS) compute-in-memory (CIM) systems, the two's-complement (2SC) format provides better area efficiency than the sign-and-magnitude (SNM) one. However, the 2SC format exacerbates the challenges of AMS-CIM systems, suffering from significant DNN accuracy drop under process variations and high computation currents from activating multiple WLs. In the 2SC format, '0' and '1' are nearly balanced for all logical-order bits, unlike '0'-skewed higher-order bits in the SNM format. Consequently, the 2SC-based AMS-CIM systems have much more on-cells than the SNM-based counterpart, deteriorating the above challenges. We propose Skew-CIM, a software-hardware co-design technique to relax these challenges. Our proposed weight skewing (WESK) breaks the '0' and '1' balance at the software level. The potential accuracy drops resulting from WESK are successfully compensated by retraining DNNs. The offsets caused by WESK can be easily corrected using online hardware-level processing. Our Skew-CIM technique can be applied to most AMS-CIM systems with memories showing large on-off cell current ratios. As an example, we use it in a custom-designed 8T-SRAM-based CIM device, demonstrating a significant reduction in the DNN classification error by 7.6 times compared to the 2SC-based AMS-CIM without our Skew-CIM technique. Furthermore, our Skew-CIM markedly enhances energy efficiency by up to 39.9%, outperforming conventional SNM-based AMS-CIM systems.

Original languageEnglish (US)
Pages (from-to)2067-2078
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number5
StatePublished - May 1 2024


  • analog computing
  • Computation-in-memory (CIM)
  • deep neural network
  • skewed weights
  • two's complement

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering


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