Photonic Processor for Fully Discretized Neural Networks

Abstract

Machine learning is now moving towards, and will become prevalent in, fog-computing and real-time computing environments. To this end, much machine-learning-at-the-edge research has focused on efficient neural network architectures, giving rise to efficient approximations of fixed-point neural networks, called discretized neural networks. While higher performing than their fixed and floating-point counterparts, discretized neural networks still have an existing bottleneck at the neuron's accumulation of products, called the popcount. This bottleneck sets an upper bound on performance regardless of neural network architecture. We address the popcount bottleneck by introducing a photonic discretized neural network processor. This processor minimizes the popcount bottleneck, thereby maximizing neural network computational throughput. Additionally, it offers potential for performance enhancement through simultaneous convolution operations enabled by wavelength division multiplexing. We show that the photonic architecture is capable of increasing performance by 700% and 100% when compared to state-of-the-art digital and analog architectures, respectively.