Digital Versus Analog Artificial Intelligence Accelerators: Advances, trends, and emerging designs

Jae Sun Seo, Jyotishman Saikia, Jian Meng, Wangxin He, Han Sok Suh, Anupreetham, Yuan Liao, Ahmed Hasssan, Injune Yeo

Research output: Contribution to specialist publicationArticle

Abstract

For state-of-the-art artificial intelligence (AI) accelerators, there have been large advances in both all-digital and analog/mixed-signal circuit-based designs. This article presents a practical overview and comparison of recent digital and analog AI accelerators. We first introduce hardware-efficient AI algorithms, which have been targeted for many AI hardware designs. Next, we present a survey of 1) all-digital AI accelerators, including designs with new dataflow, low precision, and sparsity, and 2) analog/mixed-signal AI accelerators featuring switch-capacitor circuits and in-memory computing (IMC) with ADCs. Recent advances of AI accelerators in both digital and analog design approaches are summarized, and emerging AI accelerator designs are discussed.

Original languageEnglish (US)
Pages65-79
Number of pages15
Volume14
No3
Specialist publicationIEEE Solid-State Circuits Magazine
DOIs
StatePublished - Sep 5 2022
Externally publishedYes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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