Existing photonic matrix processers are too small to tackle relevant problems. Here, I review our group’s recent work on scaling up analog photonic platforms. This work includes iterative advances to old approaches (accurate methods to calibrate MZI meshes), experimental demonstrations of recent proposals (a VCSEL array-based coherent detection ONN and a single-shot ONN based on reconfigurable free-space optical fan-out and weighting), and entirely new architectures (WDM-powered and RF-photonic fiber circuits for edge computing). The lessons learned from studying this diverse array of approaches helps inform the future development of photonic hardware for computation.
We demonstrated a large-scale space-time-multiplexed homodyne optical neural network (ONN) using arrays of high-speed (GHz) vertical-cavity surface-emitting lasers (VCSELs). Injection locking enables precise phase control over tens of VCSEL devices simultaneously, facilitating photoelectric-multiplication-based matrix operations and all-optical nonlinearity, operating at the quantum-noise limit. Our VCSEL transmitters exhibit ultra-high electro-optic conversion efficiency (Vπ=4 mV), allowing neural encoding at 5 attojoule/symbol. Three-dimensional neural connectivity allows parallel computing. The full-system energy efficiency reaches 7 fJ/operation, which is >100-fold better than the state-of-the-art digital microprocessors and other ONN demonstrations. Digit classification is achieved with an accuracy of 98% of the group truth.
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