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.
RACE-IT: A Reconfigurable Analog Computing Engine for In-Memory Transformer Acceleration
Artificial Intelligence Research Lab (AIRL), Hewlett Packard Labs, USA（人工智能研究实验室（AIRL）、惠普实验室，美国）；Technion - Israel Institute of Technology, Haifa, Israel（技术ion-以色列理工学院，海法，以色列）
QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；University of California Irvine（加州大学欧文分校）；University of California San Diego（加州大学圣地亚哥分校）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）
APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与技术学院）；Shanghai Qi Zhi Institute（上海启智研究院）；School of Integrated Circuits, Sun Yat-sen University（中山大学集成电路学院）
Queens College of the City University of New York（纽约市立大学皇后学院）；The Graduate Center, City University of New York（纽约市立大学研究生中心）；City College of the City University of New York（纽约市立学院）
