Scaling Deep-Learning Inference with Chiplet-based Architecture and Photonic Interconnects

Yuan Li, Ahmed Louri, Avinash Karanth

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

1 Scopus citations

Abstract

Chiplet-based architectures have been proposed to scale computing systems for deep neural networks (DNNs). Prior work has shown that for the chiplet-based DNN accelerators, the electrical network connecting the chiplets poses a major challenge to system performance, energy consumption, and scalability. Some emerging interconnect technologies such as silicon photonics can potentially overcome the challenges facing electrical interconnects as photonic interconnects provide high bandwidth density, superior energy efficiency, and ease of implementing broadcast and multicast operations that are prevalent in DNN inference. In this paper, we propose a chiplet-based architecture named SPRINT for DNN inference. SPRINT uses a global buffer to simplify the data transmission between storage and computation, and includes two novel designs: (1) a reconfigurable photonic network that can support diverse communications in DNN inference with minimal implementation cost, and (2) a customized dataflow that exploits the ease of broadcast and multicast feature of photonic interconnects to support highly parallel DNN computations. Simulation studies using ResNet50 DNN model show that SPRINT achieves 46% and 61% execution time and energy consumption reduction, respectively, as compared to other state-of-the-art chiplet-based architectures with electrical or photonic interconnects.

Original languageEnglish (US)
Title of host publication2021 58th ACM/IEEE Design Automation Conference, DAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages931-936
Number of pages6
ISBN (Electronic)9781665432740
DOIs
StatePublished - Dec 5 2021
Event58th ACM/IEEE Design Automation Conference, DAC 2021 - San Francisco, United States
Duration: Dec 5 2021Dec 9 2021

Publication series

NameProceedings - Design Automation Conference
Volume2021-December
ISSN (Print)0738-100X

Conference

Conference58th ACM/IEEE Design Automation Conference, DAC 2021
Country/TerritoryUnited States
CitySan Francisco
Period12/5/2112/9/21

Keywords

  • accelerator
  • chiplet
  • deep learning
  • silicon photonics

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation

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