EAGER:Scalable Photonic AI Accelerators Based on Photoelectric Multiplication

科学中最深层次的问题之一是生物认知是如何工作的。传统上，计算机科学是神经科学和心理学的研究领域，近年来，计算机科学通过“深度学习”领域揭示了这一问题。深度学习使用称为神经网络的计算机算法来执行各种任务--例如人脸识别、医疗诊断、汽车驾驶--长期以来一直被认为是计算机的难点，正在改变包括物流、制造、医疗保健和金融在内的许多行业。然而，即使在现代计算机上运行神经网络也是非常昂贵的。为了充分释放深度学习的潜力，本研究将探索一个新的概念：光学神经网络。通过在专用光学硬件上运行神经网络，有可能将速度提高至少1000倍，并将能耗降低至少1000倍。该计划将研究这一概念的可行性，为未来更广泛的技术开发铺平道路。如果实现，光学神经网络将允许研究人员开发更大、更复杂的深度学习模型，可能会打开全新的深度学习应用，这些应用超出了当今计算机的能力。基于深度神经网络(DNN)的人工智能(AI)给许多领域带来了革命性的变化，但也是有代价的：DNN是非常计算和功率密集型的。推动人工智能革命的是可用计算性能的指数增长，这使得DNN能够应用于日益复杂的任务。然而，随着摩尔定律失去动力，这一趋势不会持续太久；因此，开发AI硬件的替代平台变得尤为迫切。这位热心人士将研究一类光神经网络(ONN)，它利用光子学的独特优势，并承诺比传统电子产品的吞吐量和能源消耗提高数量级。三项关键任务是：(I)系统级架构研究，以预测ONN在实际工作负载下的性能收益；(Ii)硬件分析和可行性研究；以及(Iii)ONN基本限制的调查。研究活动包括建模、数值分析和基准。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

One of the deepest questions in science is how biological cognition works. Traditionally the purview of neuroscience and psychology, in recent years computer science have shed light on it through the field of 'deep learning'. Deep learning uses computer algorithms called neural networks to perform various tasks-e.g. face recognition, medical diagnosis, automobile driving--that have long been considered difficult for computers, and is transforming many industries including logistics, manufacturing, healthcare, and finance. However, neural networks are very costly to run even on modern computers. To unlock deep learning's full potential, this research will investigate a new concept: Optical Neural Networks. By running neural networks on dedicated optical hardware, there is a potential to increase speed and reduce energy consumption by at least 1000x. This program will study the feasibility of this concept to pave the way for more extensive technology development in the future. If realized, Optical Neural Networks will allow researchers to develop significantly larger, more complex deep learning models that may open up entirely new deep learning applications that are beyond the capabilities of present-day computers. Artificial intelligence (AI) based on deep neural networks (DNNs) has revolutionized a wide range of fields, but at a cost: DNNs are very compute- and power-intensive. Driving the AI revolution has been an exponential growth in the available compute performance, which has enabled the application of DNNs to increasingly complex tasks. However, as Moore's Law runs out of steam, this trend cannot continue for long; therefore, the development of alternative platforms for AI hardware has become especially urgent. This EAGER will study a class of optical neural networks (ONNs) that harness the unique advantages of photonics and promise orders-of-magnitude throughput- and energy-consumption improvements over conventional electronics. Three key tasks are: (i) a system-level architecture study to predict the ONN's performance gains on realistic workloads, (ii) a hardware analysis and feasibility study, and (iii) an investigation into the fundamental limits of ONNs. Research activities include modeling, numerical analysis, and benchmarking.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.