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