Low voltage time-based matrix multiplier-and-accumulator for neural computing system

Sungjin Hong, Heechai Kang, Jusung Kim, Kunhee Cho

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A time-based matrix multiply-and-accumulate (MAC) operation for a neural computing system is described. A simple and compact time-based matrix MAC structure is proposed that can perform multiplication and accumulation simultaneously in a single multiplier structure, and the hardware complexity is not affected by the matrix input size. To enhance the linearity of the weight factor, an offset-free pulse-width modulator is introduced. The proposed MAC architecture operates at a low supply voltage of 0.5 V while it consumes MAC energy of 0.38 pJ with a 32 nm low-power (LP) predictive technology model (PTM) CMOS process. In addition, the near-subthreshold operation can remove the level shifter to interface between the MAC operator and digital circuits such as static random-access-memory (SRAM) because both can utilize the same level of the supply voltage. The proposed MAC is based on a digital intensive pulse-width modulation, and thus it can further improve its performance and area with more advanced technologies.

Original languageEnglish
Article number2138
Pages (from-to)1-12
Number of pages12
JournalElectronics (Switzerland)
Volume9
Issue number12
DOIs
StatePublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • MAC
  • Matrix multiplier
  • Near-subthreshold
  • Neural computing
  • Neural network
  • Time-based analog matrix multiplier

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