Abstract

The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic engines aspire to synergize the low-power and high-bandwidth credentials of light-based deployments with novel architectures, towards surpassing the computing performance of their electronic counterparts. In this paper, we review recent progress in integrated photonic neuromorphic architectures and analyze the architectural and photonic hardware-based factors that limit their performance. Subsequently, we present our approach towards transforming silicon coherent neuromorphic layouts into high-speed and high-accuracy Deep Learning (DL) engines by combining robust architectures with hardware-aware DL training. Circuit robustness is ensured through a crossbar layout that circumvents insertion loss and fidelity constraints of state-of-the-art linear optical designs. Concurrently, we employ DL training models adapted to the underlying photonic hardware, incorporating noise- and bandwidth-limitations together with the supported activation function directly into Neural Network (NN) training. We validate experimentally the high-speed and high-accuracy advantages of hardware-aware DL models when combined with robust architectures through a SiPho prototype implementing a single column of a 4:4 photonic crossbar. This was utilized as the pen-ultimate hidden layer of a NN, revealing up to 5.93% accuracy improvement at 5GMAC/sec/axon when noise-aware training is enforced and allowing accuracies of 99.15% and 79.8% for the MNIST and CIFAR-10 classification tasks. Channel-aware training was then demonstrated by integrating the frequency response of the photonic hardware in NN training, with its experimental validation with the MNIST dataset revealing an accuracy increase of 12.93% at a record-high rate of 25GMAC/sec/axon.

Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics

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Opt. Express 30(7) 10664-10671 (2022)

Neuromorphic photonic technologies and architectures: scaling opportunities and performance frontiers [Invited]

George Dabos, Dimitris V. Bellas, Ripalta Stabile, Miltiadis Moralis-Pegios, George Giamougiannis, Apostolos Tsakyridis, Angelina Totovic, Elefterios Lidorikis, and Nikos Pleros
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At the intersection of optics and deep learning: statistical inference, computing, and inverse design

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Adv. Opt. Photon. 14(2) 209-290 (2022)

All-optical nonlinear activation function for photonic neural networks [Invited]

Mario Miscuglio, Armin Mehrabian, Zibo Hu, Shaimaa I. Azzam, Jonathan George, Alexander V. Kildishev, Matthew Pelton, and Volker J. Sorger
Opt. Mater. Express 8(12) 3851-3863 (2018)

Silicon photonic architecture for training deep neural networks with direct feedback alignment

Matthew J. Filipovich, Zhimu Guo, Mohammed Al-Qadasi, Bicky A. Marquez, Hugh D. Morison, Volker J. Sorger, Paul R. Prucnal, Sudip Shekhar, and Bhavin J. Shastri
Optica 9(12) 1323-1332 (2022)