Progress on photonic tensor processors based on time multiplexing and photoelectric multiplication

Ryan Hamerly; Alexander Sludds; Liane Bernstein; Vivienne Sze; Joel Emer; Marin Soljacic; Dirk Englund

doi:10.1117/12.2576990

5 March 2021 Progress on photonic tensor processors based on time multiplexing and photoelectric multiplication

Ryan Hamerly, Alexander Sludds, Liane Bernstein, Vivienne Sze, Joel Emer, Marin Soljacic, Dirk Englund

Proceedings Volume 11680, Physics and Simulation of Optoelectronic Devices XXIX; 116800E (2021) https://doi.org/10.1117/12.2576990
Event: SPIE OPTO, 2021, Online Only

Abstract

Optical approaches to machine learning rely heavily on programmable linear photonic circuits. Since the performance and energy efficiency scale with size, a major challenge is overcoming scaling roadblocks to the photonic technology. Recently, we proposed an optical neural network architecture based on coherent detection. This architecture has several scaling advantages over competing approaches, including linear (rather than quadratic) chip-area scaling and constant circuit depth. We review the fundamental and technological limits to the energy consumption in this architecture, which shed light on the quantum limits to analog computing, which are distinct from the thermodynamic (e.g. Landauer) limits to digital computing. Lastly, we highlight a recent "digital" implementation of our architecture, which sheds light on the scaling challenges associated with controlling aberrations in the free-space optical propagation.

Conference Presentation

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Ryan Hamerly, Alexander Sludds, Liane Bernstein, Vivienne Sze, Joel Emer, Marin Soljacic, and Dirk Englund "Progress on photonic tensor processors based on time multiplexing and photoelectric multiplication", Proc. SPIE 11680, Physics and Simulation of Optoelectronic Devices XXIX, 116800E (5 March 2021); https://doi.org/10.1117/12.2576990

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