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

SHF：媒介：协作研究：用于节能异构多核架构的光子神经网络加速器

基本信息

批准号：
1901165
负责人：
Ahmed Louri
金额：
$ 60万
依托单位：
George Washington University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901165&HistoricalAwards=false
关键词：
SHF Medium Collaborative Research Photonic

项目摘要

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.

深度学习体系结构，例如卷积神经网络和经常性神经网络，在许多现代化的人工智能中（例如图像分类和语音识别）上实现了前所未有的，有时超级人类的精度。然而，在这些渴望能源的机器学习体系结构中，功率耗散是一个主要问题，并且需要减少其设计，以提供更节能的硬件和机器学习算法的组合。越来越强调利用并行性和专业化来提高性能和能源效率。为了大大降低功耗，已经提出了硅光子学来提高每瓦的性能，而与电气实施相比，该项目利用光子技术和异质多孔来设计深水网络加速器，以改善各种机器学习应用中的并行性，一致性，能源效率和可伸缩性。该项目的第一个任务与可以实现加速器功能的光子设备的表征和识别有关，例如多重和蓄积，总和和其他算术操作。然后将表征的设备插入用于实现加速器功能的单层和多层光子拓扑中。该项目的第二个任务在拟议的光子神经网络加速器上实现了各种类型的深度学习架构，以最大程度地提高光子技术所提供的收益。该项目的第三个任务构建了广泛的模拟和建模基础架构，该基础架构结合了前两个步骤中开发的光子技术，网络体系结构，加速器功能和机器学习算法，以确认由Photonic Neur-NET-Net Workhator启用的能源消耗大量降低。因此，由于其横切性质，预计它将对下一代多层体系结构的设计产生深远的影响。它将在多个领域促进新的研究方向，涵盖计算机架构，光学技术，机器学习算法和应用程序。这项研究还将通过将发现与教学和培训相结合，在教育中发挥重要作用。所有研究发现和模拟工具包将通过会议和期刊出版物以及专门的网站将其传播到社区。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

An Approximate Communication Framework for Network-on-Chips

DOI：
10.1109/tpds.2020.2968068
发表时间：
2020-06
期刊：
IEEE Transactions on Parallel and Distributed Systems
影响因子：
5.3
作者：
Yuechen Chen;A. Louri
通讯作者：
Yuechen Chen;A. Louri

SPACX: Silicon Photonics-based Scalable Chiplet Accelerator for DNN Inference

DOI：
10.1109/hpca53966.2022.00066
发表时间：
2022-04
期刊：
2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
影响因子：
0
作者：
Yuan Li;A. Louri;Avinash Karanth
通讯作者：
Yuan Li;A. Louri;Avinash Karanth

PIXEL: Photonic Neural Network Accelerator

DOI：
10.1109/hpca47549.2020.00046
发表时间：
2020-02
期刊：
2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)
影响因子：
0
作者：
Kyle Shiflett;Dylan Wright;Avinash Karanth;A. Louri
通讯作者：
Kyle Shiflett;Dylan Wright;Avinash Karanth;A. Louri

SPRINT: A High-Performance, Energy-Efficient, and Scalable Chiplet-based Accelerator with Photonic Interconnects for CNN Inference