In-memory photonic dot-product engine with electrically programmable weight banks
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...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
20 May 2023
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| In: |
Nature Communications
Year: 2023, Volume: 14, Pages: 1-10 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-023-38473-x |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41467-023-38473-x Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41467-023-38473-x 
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| Author Notes: | Wen Zhou, Bowei Dong, Nikolaos Farmakidis, Xuan Li, Nathan Youngblood, Kairan Huang, Yuhan He, C. David Wright, Wolfram H.P. Pernice, Harish Bhaskaran |
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| 520 | |a 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%. | ||
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| 700 | 1 | |a Li, Xuan |e VerfasserIn |4 aut | |
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