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An In-memory Booth Multiplier Based on Non-volatile Memory for Neural Network Applications

Published: 31 May 2023 Publication History

Abstract

Neural network (NN) is one of the most significant methods to accomplish complex targets, which is widely used in image recognition, natural language processing and so on. NN demands tremendous amount of parallel Multiply-and-accumulation (MAC) operations that would affect the speed and power efficiency. Thus, how to accelerate MAC and reduce the power consumption, especially for multiplication, is a critical concern. Perpendicular-anisotropy spin-orbit torque (SOT) magnetic random access memory (MRAM) with spin transfer torque (STT) assisted is leveraged in this work, which is perfect to be used for NN because of its non-volatility, power efficiency and ultrafast operation. In addition, Booth arithmetic is an excellent method to reduce the partial products of the multiplication for acceleration. In this work, an in-memory Booth multiplier based on MRAM is designed and analyzed through simulation. Compared with the in-SRAM counterpart, our design saved 70.4% energy of the decoding part, which shows great improvement.

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Received 21 September 2022; accepted 14 November 2022

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  1. An In-memory Booth Multiplier Based on Non-volatile Memory for Neural Network Applications

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      cover image ACM Conferences
      NANOARCH '22: Proceedings of the 17th ACM International Symposium on Nanoscale Architectures
      December 2022
      140 pages
      ISBN:9781450399388
      DOI:10.1145/3565478
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 31 May 2023

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      Author Tags

      1. MRAM
      2. booth multiplier
      3. multiply-and-accumulation
      4. neural network

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      • the National Natural Science Foundation of China
      • the Jount Funds of the National Natural Science Foundation of China

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      NANOARCH '22 Paper Acceptance Rate 25 of 31 submissions, 81%;
      Overall Acceptance Rate 55 of 87 submissions, 63%

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