FET: Small: LightRidge: End-to-end Agile Design for Diffractive Optical Neural Networks
FET:小型:LightRidge:衍射光神经网络的端到端敏捷设计
基本信息
- 批准号:2321404
- 负责人:
- 金额:$ 59.94万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-10-01 至 2026-09-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Recently, there have been increasing efforts to advance emerging technologies, which bring significant advantages for machine learning (ML) in terms of power efficiency, computational efficiency, and sustainability. With the considerable benefits in energy efficiency, there are significant interests in leveraging optical computing into applications, such as medical sensing, security screening, drug detection, and autonomous driving. Specifically, optical computing offers unique advantages in power efficiency and extreme computation speed, leading to significant performance improvements compared to digital computing systems for ML tasks. This project aims to develop an end-to-end design infrastructure to advance optical computing for ML, covering from low-level physics to algorithms to full-stack system design. This will generate broader impacts in cross-disciplinary research and real-world application fields from physics to computer science to ML. This project will produce an open-source design infrastructure, LightRidge, and conference tutorials to facilitate technology transfers and fruitful industry-academia interactions in a multidisciplinary community.This project aims to develop an open-source, end-to-end design infrastructure, LightRidge, to explore and advance Diffractive Deep Neural Networks (DONNs) in real-world ML tasks. DONNs utilize the free-space light diffraction to form an optical feed-forward network like conventional DNNs architecture, which can host millions of neurons in each layer that are interconnected with those in neighboring layers, offering orders of magnitude energy efficiency improvements over general-purpose processor and domain-specific accelerators. However, there are several critical technical barriers in the design, training, exploration, and hardware deployment of DONNs. Thus, this project will produce an agile end-to-end design and fabrication programming framework LightRidge, consisting of precise, versatile, and differentiable optical physics kernels powered by domain-specific high-performance-computing developments, with novel physics-aware hardware-software codesign methodologies to strengthen the correlations between algorithm modeling and physical hardware. This project will also develop an intelligent and efficient design space exploration (DSE) engine LightRidge-DSE, to enable architectural and fabrication parameters exploration, monolithic on-chip DONNs integration, and demonstrate real-world all-optical ML tasks. Finally, LightRidge will be fully released as an open-source hardware project, which will contribute to multidisciplinary research domains such as physics, electrical engineering, computer science, and can be used as a new education platform.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.
最近,人们越来越多地努力推进新兴技术,这些技术在功率效率、计算效率和可持续性方面为机器学习(ML)带来了显著的优势。由于在能源效率方面有相当大的好处,人们对将光学计算利用到医疗传感、安全筛查、药物检测和自动驾驶等应用中有很大的兴趣。具体来说,光学计算在功率效率和极端计算速度方面提供了独特的优势,与ML任务的数字计算系统相比,可以显著提高性能。该项目旨在开发端到端设计基础设施,以推进机器学习的光学计算,涵盖从低级物理到算法再到全栈系统设计。这将在跨学科研究和现实世界的应用领域产生更广泛的影响,从物理学到计算机科学再到机器学习。该项目将产生一个开源的设计基础设施、LightRidge和会议教程,以促进多学科社区的技术转让和富有成效的产学界互动。该项目旨在开发一个开源的端到端设计基础设施LightRidge,以在现实世界的ML任务中探索和推进衍射深度神经网络(donn)。donn利用自由空间光衍射形成一个像传统dnn结构一样的光前馈网络,它可以在每层中容纳数百万个神经元,这些神经元与相邻层中的神经元相互连接,比通用处理器和特定领域加速器提供数量级的能效改进。然而,在donn的设计、训练、探索和硬件部署方面存在一些关键的技术障碍。因此,该项目将产生一个灵活的端到端设计和制造编程框架LightRidge,由精确、通用和可微分的光学物理内核组成,由特定领域的高性能计算开发提供支持,具有新颖的物理感知硬件-软件协同设计方法,以加强算法建模和物理硬件之间的相关性。该项目还将开发一种智能高效的设计空间探索(DSE)引擎LightRidge-DSE,以实现架构和制造参数探索,单片片上donn集成,并演示真实世界的全光ML任务。最后,LightRidge将作为一个开源硬件项目全面发布,这将有助于物理、电气工程、计算机科学等多学科研究领域,并可作为一个新的教育平台。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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Cunxi Yu其他文献
Survey on Applications of Formal Methods in Reverse Engineering and Intellectual Property Protection
形式化方法在逆向工程和知识产权保护中的应用综述
- DOI:
10.1007/s41635-018-0044-3 - 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
S. Keshavarz;Cunxi Yu;S. Ghandali;Xiaolin Xu;Daniel E. Holcomb - 通讯作者:
Daniel E. Holcomb
Dataless Quadratic Neural Networks for the Maximum Independent Set Problem
无数据二次神经网络求解最大独立集问题
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Ismail R. Alkhouri;Cedric Le Denmat;Yingjie Li;Cunxi Yu;Jia Liu;Rongrong Wang;Alvaro Velasquez - 通讯作者:
Alvaro Velasquez
Reverse engineering of irreducible polynomials in GF(2m) arithmetic
GF(2m) 算法中不可约多项式的逆向工程
- DOI:
- 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Cunxi Yu;Daniel E. Holcomb;M. Ciesielski - 通讯作者:
M. Ciesielski
Logic Debugging of Arithmetic Circuits
算术电路的逻辑调试
- DOI:
10.1109/isvlsi.2015.16 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
S. Ghandali;Cunxi Yu;Duo Liu;W. Brown;M. Ciesielski - 通讯作者:
M. Ciesielski
FlowTune: Practical Multi-armed Bandits in Boolean Optimization
- DOI:
10.1145/3400302.3415615 - 发表时间:
2020-11 - 期刊:
- 影响因子:0
- 作者:
Cunxi Yu - 通讯作者:
Cunxi Yu
Cunxi Yu的其他文献
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{{ truncateString('Cunxi Yu', 18)}}的其他基金
Collaborative Research: SHF: Medium: Differentiable Hardware Synthesis
合作研究:SHF:媒介:可微分硬件合成
- 批准号:
2403134 - 财政年份:2024
- 资助金额:
$ 59.94万 - 项目类别:
Standard Grant
Collaborative Research: FMitF: Track I: DeepSmith: Scheduling with Quality Guarantees for Efficient DNN Model Execution
合作研究:FMitF:第一轨:DeepSmith:为高效 DNN 模型执行提供质量保证的调度
- 批准号:
2349461 - 财政年份:2023
- 资助金额:
$ 59.94万 - 项目类别:
Standard Grant
SHF: Small: Boosting Reasoning in Boolean Networks with Attributed Graph Learning
SHF:小:通过属性图学习增强布尔网络的推理
- 批准号:
2350186 - 财政年份:2023
- 资助金额:
$ 59.94万 - 项目类别:
Standard Grant
CAREER: OneSense: One-Rule-for-All Combinatorial Boolean Synthesis via Reinforcement Learning
职业:OneSense:通过强化学习进行一刀切的组合布尔综合
- 批准号:
2349670 - 财政年份:2023
- 资助金额:
$ 59.94万 - 项目类别:
Continuing Grant
CAREER: OneSense: One-Rule-for-All Combinatorial Boolean Synthesis via Reinforcement Learning
职业:OneSense:通过强化学习进行一刀切的组合布尔综合
- 批准号:
2047176 - 财政年份:2021
- 资助金额:
$ 59.94万 - 项目类别:
Continuing Grant
SHF: Small: Boosting Reasoning in Boolean Networks with Attributed Graph Learning
SHF:小:通过属性图学习增强布尔网络的推理
- 批准号:
2008144 - 财政年份:2020
- 资助金额:
$ 59.94万 - 项目类别:
Standard Grant
Collaborative Research: FMitF: Track I: DeepSmith: Scheduling with Quality Guarantees for Efficient DNN Model Execution
合作研究:FMitF:第一轨:DeepSmith:为高效 DNN 模型执行提供质量保证的调度
- 批准号:
2019336 - 财政年份:2020
- 资助金额:
$ 59.94万 - 项目类别:
Standard Grant
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