An IMPLY-based Memristive Multiplier for Computing-in-Memory Systems with Weight-Stationary CNN Acceleration

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First Author:W. Liang, J. Xu,

Correspondence Author:C. Wang*

Co-author:Y. Zhao, Z. Shen, G. Yu, Y. He, and

Journal:IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA 2022)

Included Journals:EI

Discipline:Engineering

First-Level Discipline:Electronic Science And Technology

Document Type:C

Abstract:Adders and multipliers based on memristive Material Implication (IMPLY) logic are widely used in primary building blocks of Arithmetic Logic Unit (ALU). To solve the issue that the existing IMPLY-based multipliers cannot protect the input operands, this paper presents a novel data non-destructive memristive IMPLY-based semi-parallel multiplier for Computing-in-Memory (CIM) systems, by assigning function-specific memristors for data-protection and introducing additional switches for higher parallelism. Simulation results show that the proposed multiplier can achieve 30% faster than conventional semi-parallel design and 9.1% less memristors against the state-of-art semi-serial design for 4-bit multiplication, while preventing the input weight from destruction as required by CNN weight reuse.