Efficient Signed Arithmetic Multiplication on Memristor-based Crossbar

Mahdi Zahedi*, Taha Shahroodi, Stephan Wong, Said Hamdioui

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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The vast potential of memristor-based computation-in-memory (CIM) engines has mainly triggered the mapping of best-suited applications. Nevertheless, with additional support, existing applications can also benefit from CIM. In particular, this paper proposes an energy and area-efficient CIM-based methodology to perform arithmetic signed matrix multiplications. Our approach combines a) the mapping of the signed operands on the 1T1R crossbar, and b) the augmentation of the periphery with customized circuits to support the execution of shift and accumulate needed for the arithmetic operations. The operand mapping is performed without the need for sign extension; hence, reducing the required memory size. To demonstrate the superiority of our scheme as compared with the state-of-the-art, simulations are performed for different case studies including a neural network and two kernels which are taken from the Polybench/C benchmark suite. The results show that our approach achieves up to 8× energy-saving and 3× area-saving compared with other CIM-based prior works.

Original languageEnglish
Pages (from-to)33964-33978
Number of pages15
JournalIEEE Access
Publication statusPublished - 2023


  • computation-in-memory
  • Memristor
  • signed computation


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