PIMBALL: Binary neural networks in spintronic memory

Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury, Masoud Zabihi, Zhengyang Zhao, Jian Ping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/ software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.

Original languageEnglish (US)
Article numberA41
JournalACM Transactions on Architecture and Code Optimization
Issue number4
StatePublished - Oct 2019

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.


  • Binary neural networks
  • Computational random access memory
  • Non-volatile memory
  • Processing in memory


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