PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator
This work addresses the problem of efficient neural network inference for applications requiring high speed and low energy, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of low-latency, high-throughput convolutional neural network inference by proposing PhotoFourier, a photonic accelerator based on Joint Transform Correlator, which achieves more than 28X better energy-delay product compared to state-of-the-art photonic neural network accelerators.
The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This 'free' convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28X better energy-delay product compared to state-of-art photonic neural network accelerators.