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This paper analyzes the performance and energy efficiency of Netcast, arecently proposed optical neural-network architecture designed for edgecomputing. Netcast performs deep neural network inference by dividing thecomputational task into two steps, which are split between the server and(edge) client: (1) the server employs a wavelength-multiplexed modulator arrayto encode the network's weights onto an optical signal in an analogtime-frequency basis, and (2) the client obtains the desired matrix-vectorproduct through modulation and time-integrated detection. The simultaneous useof wavelength multiplexing, broadband modulation, and integration detectionallows large neural networks to be run at the client by effectively pushing theenergy and memory requirements back to the server. The performance and energyefficiency are fundamentally limited by crosstalk and detector noise,respectively. We derive analytic expressions for these limits and performnumerical simulations to verify these bounds.

Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks