In-memory photonic dot-product engine with electrically programmable weight banks

Wen Zhou, Bowei Dong, Nikolaos Farmakidis, Xuan Li, Nathan Youngblood, Kairan Huang, Yuhan He, C. David Wright, Wolfram H. P. Pernice and Harish Bhaskaran ()
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Wen Zhou: University of Oxford
Bowei Dong: University of Oxford
Nikolaos Farmakidis: University of Oxford
Xuan Li: University of Oxford
Nathan Youngblood: University of Oxford
Kairan Huang: University of Oxford
Yuhan He: University of Oxford
C. David Wright: University of Exeter
Wolfram H. P. Pernice: University of Münster
Harish Bhaskaran: University of Oxford

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic–electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic–electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.

Date: 2023
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DOI: 10.1038/s41467-023-38473-x

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