SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs

Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Ishan Thakkar, Ahmad Salehi, Todd Hastings

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Convolutional Neural Networks (CNNs) are used extensively for artificial intelligence applications due to their record-breaking accuracy. For efficient and swift hardware-based acceleration, CNNs are typically quantized to have integer input/weight parameters. The acceleration of a CNN inference task uses convolution operations that are typically transformed into vector-dot-product (VDP) operations. Several photonic microring resonators (MRRs) based hardware architectures have been proposed to accelerate integer-quantized CNNs with remarkably higher throughput and energy efficiency compared to their electronic counterparts. However, the existing photonic MRR-based analog accelerators exhibit a very strong trade-off between the achievable input/weight precision and VDP operation size, which severely restricts their achievable VDP operation size for the quantized input/weight precision of 4 bits and higher. The restricted VDP operation size ultimately suppresses computing throughput to severely diminish the achievable performance benefits. To address this shortcoming, we for the first time present a merger of stochastic computing and MRR-based CNN accelerators. To leverage the innate precision flexibility of stochastic computing, we invent an MRR-based optical stochastic multiplier (OSM). We employ multiple OSMs in a cascaded manner using dense wavelength division multiplexing, to forge a novel Stochastic Computing based Optical Neural Network Accelerator (SCONNA). SCONNA achieves significantly high throughput and energy efficiency for accelerating inferences of high-precision quantized CNNs. Our evaluation for the inference of four modern CNNs at 8-bit input/weight precision indicates that SCONNA provides improvements of up to 66.5×, 90× and 91× in frames-per-second (FPS), FPS/W and FPS/W/mm2 respectively, on average over two photonic MRR-based analog CNN accelerators from prior work, with Top-1 accuracy drop of only up to 0.4% for large CNNs and up to 1.5% for small CNNs. We developed a transaction-level, event-driven python-based simulator for the evaluation of SCONNA and other accelerators (https://github.com/uky-UCAT/SC_ONN_SIM.git).

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
Pages546-556
Number of pages11
ISBN (Electronic)9798350337662
DOIs
StatePublished - 2023
Event37th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023 - St. Petersburg, United States
Duration: May 15 2023May 19 2023

Publication series

NameProceedings - 2023 IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023

Conference

Conference37th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2023
Country/TerritoryUnited States
CitySt. Petersburg
Period5/15/235/19/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems

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