Coherent Photonic neuromorphic computing for high-speed Deep Learning applications (Invited)

The emergence of demanding machine learning and AI workloads in modern computational systems and Data Centers (DC) has fueled a drive towards custom hardware, designed to accelerate Multiply-Accumulate (MAC) operations. In this context, neuromorphic photonics have recently attracted attention as a promising technological candidate, that can transfer photonics low-power, high bandwidth credentials in neuromorphic hardware implementations. However, the deployment of such systems necessitates progress in both the underlying constituent building blocks as well as the development of deep learning training models that can take into account the physical properties of the employed photonic components and compensate for their non-ideal performance. Herein, we present an overview of our progress in photonic neuromorphic computing based on coherent layouts, that exploits the phase of the light traversing the photonic circuitry both for sign representation and matrix manipulation. Our approach breaks-through the direct trade-off of insertion loss and modulation bandwidth of State-Of-The-Art coherent architectures and allows high-speed operation in reasonable energy envelopes. We present a silicon-integrated coherent linear neuron (COLN) that relies on electro-absorption modulators (EAM) both for its on-chip data generation and weighting, demonstrating a record-high 32 GMAC/sec/axon compute linerate and an experimentally obtained accuracy of 95.91% in the MNIST classification task. Moreover, we present our progress on component specific neuromorphic circuitry training, considering both the photonic link thermal noise and its channel response. Finally, we present our roadmap on scaling our architecture using a novel optical crossbar design towards a 32x32 layout that can offer >32 GMAC/sec/axon computational power in similar to 0.09 pJ/MAC.