A Low-Dissipation and Scalable GEMM Accelerator with Silicon Nitride Photonics

Venkata Sai Praneeth Karempudi, Sairam Sri Vatsavai, Ishan Thakkar, Oluwaseun Adewunmi Alo, Jeffrey Todd Hastings, Justin Scott Woods

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

Abstract

Over the past few years, several microring resonator (MRR)-based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs), which are found in abundance in deep learning workloads. These architectures have dramatically grown in popularity because they offer exceptional throughput and energy efficiency compared to their electronic counterparts. However, such architectures, due to their traditional realization based on the silicon-on-insulator (SOI) material platform, face two shortcomings. First, the high-index contrast of the SOI platform incurs high scattering losses, which mandates the provisioning of high optical input power. Second, SOI waveguides are susceptible to two-photon absorption (TPA), which can incur substantial optical signal losses at moderate-to-high signal fan-in. These shortcomings have severely detrimental effects on the achievable parallelism, throughput, and energy efficiency of SOI MRR-based GEMM accelerators. To address these shortcomings, we present a novel Silicon Nitride (SiN)-Based Photonic GEMM Accelerator called SiNPhAR. SiNPhAR architecture employs SiN-based active and passive devices to implement analog GEMM functions. Since the SiN material exhibits lower index contrast and no TPA, the optical signal losses in our SiNPhAR architecture are very low. This advantage significantly enhances the achievable processing parallelism, throughput, and energy efficiency of SiNPhAR architecture, compared to SOI-based photonic GEMM accelerators from prior work. We quantify and compare these benefits of SiNPhAR architecture via our cross-layer evaluation for a benchmark workload comprising four modern deep neural network models. From the system-level performance analysis, SiNPhAR demonstrates at least 1.7× better throughput (frames-per-second (FPS)) while consuming at least 2.8× better energy efficiency (FPS/W) than prior SOI-based GEMM accelerators.

Original languageEnglish
Title of host publicationProceedings of the 25th International Symposium on Quality Electronic Design, ISQED 2024
ISBN (Electronic)9798350309270
DOIs
StatePublished - 2024
Event25th International Symposium on Quality Electronic Design, ISQED 2024 - Hybrid, San Francisco, United States
Duration: Apr 3 2024Apr 5 2024

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference25th International Symposium on Quality Electronic Design, ISQED 2024
Country/TerritoryUnited States
CityHybrid, San Francisco
Period4/3/244/5/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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