Open AccessStarLight: a photonic neural network accelerator featuring a hybrid mode-wavelength division multiplexing and photonic nonvolatile memory
Author(s) -
Pengxing Guo,
Niujie Zhou,
Weigang Hou,
Lei Guo
Publication year - 2022
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.468456
Subject(s) - starlight , computer science , photonics , multiplexing , optical computing , optics , electronic engineering , computer hardware , physics , telecommunications , engineering , stars , computer vision
This paper proposes StarLight, a low-power consumption and high inference throughput photonic artificial neural network (ANN) accelerator featuring the photonic 'in-memory' computing and hybrid mode-wavelength division multiplexing (MDM-WDM) technologies. Specifically, StarLight uses nanophotonic non-volatile memory and passive microring resonators (MRs) to form a photonic dot-produce engine, achieving optical 'in-memory' multiplication operation with near-zero power consumption during the inference phase. Furthermore, we design an on-chip wavelength and mode hybrid multiplexing module and scheme to increase the computational parallelism. As a proof of concept, a 4×4×4 optical computing unit featuring 4-wavelength and 4-mode is simulated with 10 Gbps, 15 Gbps and 20 Gbps data rates. We also implemented a simulation on the Iris dataset classification and achieved an inference accuracy of 96%, which is entirely consistent with the classification accuracy on a 64-bit computer. Therefore, StarLight holds promise for realizing low energy consumption hardware accelerators to address the incoming challenges of data-intensive artificial intelligence (AI) applications.