AIsim: Functional Simulator for Analog-to-Information Perceptual Systems

Hong Liu, Zheyu Liu, Fei Qiao, Po-Hung Lin, Qi Wei, Huazhong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Neural network accelerators have been extensively studied for artificial intelligent applications for recent years. Analogto-information systems (AIS), which could accelerate neural network computation in analog domain, are considered as one better alternative for achieving higher scalability and energy efficiency. Unlike operating in the digital domain, an AIS adopts analog computing units to construct the whole neural network for perceptual tasks. However, when designing an AIS, conventional HSPICE simulation is very time-consuming. In order to improve the design capacity, this paper presents a functional simulator, called AIsim for AIS. AIsim could efficiently simulate the noise characteristics in AIS while processing neural network algorithms. In particular, it could also guide the designers to explore the design space of analog computing units with various signal-to-noise ratio (SNR). Compared with conventional HSPICE simulator, AIsim can achieve more than 2000X simulation speedup with 2% results difference at most, for some typical benchmarks.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
EditorsRicardo Reis, Mircea Stan, Michael Huebner, Nikolaos Voros
PublisherIEEE Computer Society
Pages507-512
Number of pages6
ISBN (Electronic)9781509067626
DOIs
StatePublished - 20 Jul 2017
Event2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017 - Bochum, North Rhine-Westfalia, Germany
Duration: 3 Jul 20175 Jul 2017

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2017-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Conference

Conference2017 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2017
CountryGermany
CityBochum, North Rhine-Westfalia
Period3/07/175/07/17

Keywords

  • Analog-to-Information
  • functional simulator
  • neural network
  • SNR

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