Abstract
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.
Abstract (translated)
新兴人工智能应用，如聊天机器人GPT、图卷积神经网络(CNN)和其他深度神经网络，需要进行大量的训练和推理资源。当前计算机平台，如CPU、GPU和TPU，正在努力满足这些人工智能应用的需要。非相干光学计算是一种有望加速人工智能工作负载光速加速的方法。在本文中，我们展示了如何实现跨层设计来克服非相干光学计算平台上的挑战。我们描述了光学设备工程、调整电路增强和建筑创新的方法，以适应多种人工智能工作负载。我们还讨论了硬件/软件协同设计的方法，这些技术可以智能地映射和适应人工智能软件，以提高其在非相干光学计算平台上的性能。
URL
https://arxiv.org/abs/2303.12910
