Coherent VCSEL homodyne neural networks

Zaijun Chen; Alexander Sludds; Ronald Davis; Ian Christen; Liane Bernstein; Tobias Heuser; Niels Heermeier; James A. Lott; Stephan Reitzenstein; Ryan Hamerly; Dirk Englund

doi:10.1117/12.2648628

15 March 2023 Coherent VCSEL homodyne neural networks

Zaijun Chen, Alexander Sludds, Ronald Davis, Ian Christen, Liane Bernstein, Tobias Heuser, Niels Heermeier, James A. Lott, Stephan Reitzenstein, Ryan Hamerly, Dirk Englund

Author Affiliations +

Proceedings Volume 12438, AI and Optical Data Sciences IV; 1243809 (2023) https://doi.org/10.1117/12.2648628
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

We demonstrated a large-scale space-time-multiplexed homodyne optical neural network (ONN) using arrays of high-speed (GHz) vertical-cavity surface-emitting lasers (VCSELs). Injection locking enables precise phase control over tens of VCSEL devices simultaneously, facilitating photoelectric-multiplication-based matrix operations and all-optical nonlinearity, operating at the quantum-noise limit. Our VCSEL transmitters exhibit ultra-high electro-optic conversion efficiency (V_π=4 mV), allowing neural encoding at 5 attojoule/symbol. Three-dimensional neural connectivity allows parallel computing. The full-system energy efficiency reaches 7 fJ/operation, which is >100-fold better than the state-of-the-art digital microprocessors and other ONN demonstrations. Digit classification is achieved with an accuracy of 98% of the group truth.

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Citation Download Citation

Zaijun Chen, Alexander Sludds, Ronald Davis, Ian Christen, Liane Bernstein, Tobias Heuser, Niels Heermeier, James A. Lott, Stephan Reitzenstein, Ryan Hamerly, and Dirk Englund "Coherent VCSEL homodyne neural networks", Proc. SPIE 12438, AI and Optical Data Sciences IV, 1243809 (15 March 2023); https://doi.org/10.1117/12.2648628

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