SHF: Medium: Collaborative Research: Photonic Neural Network Accelerators for Energy-efficient Heterogeneous Multicore Architectures

卷积神经网络和循环神经网络等深度学习架构在人工智能的许多现代应用（如图像分类和语音识别）中取得了前所未有的，有时甚至是超人类的精度。然而，在这些高能耗的机器学习架构中，功耗是一个主要问题，降低功耗需要提供更节能的硬件和机器学习算法组合的设计。越来越强调利用并行性和专门化来提高性能和能源效率。为了大幅降低功耗，硅光子学已经被提出来提高每瓦特的性能，与电气实现相比。该项目利用光子技术和异构多核设计深度神经网络加速器，提高各种机器学习应用的并行性、并发性、能效和可扩展性。该项目的第一项任务是研究光子器件的特性和识别，这些光子器件可以实现加速器的功能，如乘法累加、求和和其他算术运算。然后将所表征的器件插入单层和多层光子拓扑中以实现加速器功能。该项目的第二项任务是在提出的光子神经网络加速器上实现各种类型的深度学习架构，以最大限度地提高光子技术带来的收益。该项目的第三个任务是建立一个广泛的仿真和建模基础设施，将前两个步骤中开发的光子技术、网络架构、加速器功能和机器学习算法相结合，以验证光子神经网络加速器能够显著降低能耗。提出的研究在光子技术，硬件架构和机器学习之间架起了一个非常重要的桥梁。因此，由于其横切性质，预计它将对下一代多核架构的设计产生深远的影响。它将在几个领域培养新的研究方向，包括计算机体系结构、光学技术、机器学习算法和应用。这项研究还将通过将发现与教学和培训结合起来，在教育方面发挥重要作用。所有的研究成果和模拟工具包将通过会议和期刊出版物以及一个专门的网站向社区传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Deep learning architectures such as convolutional neural networks and recurrent neural networks have achieved unprecedented, sometimes super-human accuracy on many modern applications in artificial intelligence, such as image classification and speech recognition. Power dissipation is however a major concern in these energy-hungry machine-learning architectures, and decreasing it requires designs that provide a more energy-efficient combination of hardware and machine-learning algorithms. There is an increased emphasis to leverage parallelism and specialization to improve performance and energy efficiency. To dramatically reduce power consumption, silicon photonics has been proposed to improve performance-per-Watt compared to electrical implementation.This project leverages photonic technology and heterogeneous multicores for the design of deep-neural network accelerators that improve parallelism, concurrency, energy efficiency and scalability in various machine-learning applications. The first task of the project is concerned with the characterization and identification of photonic devices that can implement accelerator functionalities such as multiply-and-accumulate, summation, and other arithmetic operations. The characterized devices are then inserted into single-layer and multi-layer photonic topologies for implementing accelerator functionality. The second task of the project implements various types of deep-learning architectures on the proposed photonic neural network accelerator to maximize the gains offered by the photonic technology. The third task of the project builds an extensive simulation and modeling infrastructure that combines the photonic technology, network architectures, accelerator functionality, and machine-learning algorithms developed in the previous two steps, in order to validate the significant reduction in energy consumption enabled by the photonic neural-network accelerator.The proposed research bridges a very important gap between photonic technology, hardware architecture, and machine learning. As such, and due to its cross-cutting nature, it is expected to have far-reaching impacts on the design of next-generation multicore architectures. It will foster new research directions in several areas, spanning computer architecture, optical technology, machine learning algorithms and applications. The research will also play a major role in education by integrating discovery with teaching and training. All the research findings and simulation toolkits will be disseminated to the community via conference and journal publications, and a dedicated website.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.