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.

Date Published: 9 March 2020
PDF
Proc. SPIE 11299, AI and Optical Data Sciences, 112990D (9 March 2020); doi: 10.1117/12.2546966

Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)