SWIPE: Enhancing Robustness of ReRAM Crossbars for In-memory Computing

Sujan K. Gonugondla, Ameya D. Patil, Naresh R. Shanbhag

Research output: Contribution to journalConference articlepeer-review


Crossbar-based in-memory architectures have emerged as an attractive platform for energy-efficient realization of deep neural networks (DNNs). A key challenge in such architectures is achieving accurate and efficient writes due to the presence of bitcell conductance variations. In this paper, we propose the Single-Write In-memory Program-vErify (SWIPE) method that achieves high accuracy writes for crossbar-based in-memory architectures at 5×-to-10× lower cost than standard program-verify methods. SWIPE leverages the bit-sliced attribute of crossbar-based in-memory architectures and the statistics of conductance variations to compensate for device non-idealities. Using SWIPE to write into ReRAM crossbar allows for a 2× (CIFAR-10) and 3× (MNIST) increase in storage density with < 1% loss in DNN accuracy. In particular, SWIPE compensates for 4.8×-to-7.7× higher conductance variations. Furthermore, SWIPE can be augmented with injection-based training methods in order to achieve even greater enhancements in robustness.

Original languageEnglish (US)
Article number9256661
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
StatePublished - Nov 2 2020
Externally publishedYes
Event39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States
Duration: Nov 2 2020Nov 5 2020


  • ReRAM
  • compensation
  • crossbars
  • error correction
  • in-memory
  • neural networks
  • resistive crossbars
  • variations

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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