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The last few years have seen a lot of work to address the challenge oflow-latency and high-throughput convolutional neural network inference.Integrated photonics has the potential to dramatically accelerate neuralnetworks because of its low-latency nature. Combined with the concept of JointTransform Correlator (JTC), the computationally expensive convolution functionscan be computed instantaneously (time of flight of light) with almost no cost.This 'free' convolution computation provides the theoretical basis of theproposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses amyriad of challenges posed by on-chip photonic computing in the Fourier domainincluding 1D lenses and high-cost optoelectronic conversions. The proposedPhotoFourier accelerator achieves more than 28X better energy-delay productcompared to state-of-art photonic neural network accelerators.

PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator