Electro-optic perceptron towards 10^18 MAC/J-efficient photonic neural networks (Conference Presentation)

Rubab Amin; Mario Miscuglio; Bhavin J. Shastri; Paul Prucnal; Volker J. Sorger

doi:10.1117/12.2546966

9 March 2020 Electro-optic perceptron towards 10¹⁸ MAC/J-efficient photonic neural networks (Conference Presentation)

Rubab Amin, Mario Miscuglio, Bhavin J. Shastri, Paul Prucnal, Volker J. Sorger

Proceedings Volume 11299, AI and Optical Data Sciences; 112990D (2020) https://doi.org/10.1117/12.2546966
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

Non-van Neumann compute engines such as neuromorphic electronics have shown to outperform CPUs by 3-4 orders of magnitude in terms of ‘weighted addition’, namely multiply-accumulate (MAC)-per-Joule. Here, we discuss experimental devices for a photonic neural network (NN) with an energy efficiency targeting10^18 MAC/J. We consider an electro-optic perceptron consisting of a photodetector (summation) coupled to an EO modulator (nonlinear activation function, NLAF) [George et al, Opt.Exp. 2019]. The perceptron’s efficiency is proportional to the electronic charge at the NLAF; in case of Silicon MZI modulators, this is ~10^6 charges hence the MAC/J is similar to TrueNorth. However, co-integration of emerging EO materials such as ITO into Si MZIs enables efficient modulation (e.g. VpL=0.5 V-mm [Armin et al, APL Phot. 2018]. Here we discuss latest results of a ITO-Silicon MZM with a record-low VpL=0.06 V-mm, and show noise-based NN training results of our in-house software PhotonFlow.

Conference Presentation

Citation Download Citation

Rubab Amin, Mario Miscuglio, Bhavin J. Shastri, Paul Prucnal, and Volker J. Sorger "Electro-optic perceptron towards 10¹⁸ MAC/J-efficient photonic neural networks (Conference Presentation)", Proc. SPIE 11299, AI and Optical Data Sciences, 112990D (9 March 2020); https://doi.org/10.1117/12.2546966

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