  Emerging AI applications such as ChatGPT, graph convolutional networks, and
other deep neural networks require massive computational resources for training
and inference. Contemporary computing platforms such as CPUs, GPUs, and TPUs
are struggling to keep up with the demands of these AI applications.
Non-coherent optical computing represents a promising approach for light-speed
acceleration of AI workloads. In this paper, we show how cross-layer design can
overcome challenges in non-coherent optical computing platforms. We describe
approaches for optical device engineering, tuning circuit enhancements, and
architectural innovations to adapt optical computing to a variety of AI
workloads. We also discuss techniques for hardware/software co-design that can
intelligently map and adapt AI software to improve its performance on
non-coherent optical computing platforms.
