Multi-Level Encoding and Decoding in a Scalable Photonic Tensor Processor With a Photonic General Matrix Multiply (GeMM) Compiler

Zhimu Guo, Alexander N. Tait, Bicky A. Marquez, Matthew Filipovich, Hugh Morison, Paul R. Prucnal, Lukas Chrostowski, Sudip Shekhar, Bhavin J. Shastri

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


The resurgence of artificial intelligence enabled by deep learning and high performance computing has seen a dramatic increase of demand in the accuracy of deep learning model which has come at the cost of computational complexity. The fundamental operations in deep learning models are matrix multiplications, and large scale matrix operations and data-centric tasks have experienced bottlenecks from current digital electronic hardware in terms of performance and scalability. Recent research on photonic processors have found solutions to enable applications in machine learning, neuromorphic computing and high performance computing using basic photonic processing elements on integrated silicon photonic platform. However, efficient and scalable photonic computing requires an information encoding/decoding scheme. Here, we propose a multi-level encoding and decoding scheme, and experimentally demonstrate it with a wavelength-multiplexed silicon photonic processor. We also discuss the scalability of our proposed scheme by introducing a photonic general matrix multiply compiler, and consider the effects of speed, bit precision, and noise. Our proposed scheme could be adapted to a variety of photonic information processing architectures for photonic neural networks, photonics tensor cores, and programmable photonic.

Original languageEnglish (US)
Article number8300714
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number6
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering


  • Integrated optics
  • matrix decomposition
  • matrix multiplication
  • optical computing
  • optical neural networks
  • programmable circuits


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