Sunny, Febin, authorNikdast, Mahdi, authorPasricha, Sudeep, authorACM, publisher2024-11-112024-11-112023-06-05Febin Sunny, Mahdi Nikdast, and Sudeep Pasricha. 2023. Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing. In Proceedings of 2023 ACM Great Lakes Symposium on VLSI (GLSVLSI'23), June 5-7, 2023, Knoxville, TN, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3583781.3590224https://hdl.handle.net/10217/239516Emerging 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 performance on non-coherent platforms.born digitalarticleseng© Febin Sunny, et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in GLSVLSI '23, https://dx.doi.org/10.1145/3583781.3590224.optical neural networksnon-coherent optical computingartificial intelligence acceleratorsTexthttps://doi.org/10.1145/3583781.3590224