Collaborative Research: SHF: Medium: TensorNN: An Algorithm and Hardware Co-design Framework for On-device Deep Neural Network Learning using Low-rank Tensors
合作研究:SHF:Medium:TensorNN:使用低秩张量进行设备上深度神经网络学习的算法和硬件协同设计框架
基本信息
- 批准号:1955909
- 负责人:
- 金额:$ 40万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-01 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Deep neural network (DNN) is an important Artificial Intelligence (AI) technique and it has recently gained widespread applications in numerous fields such as image recognition, machine translation, autonomous vehicles and healthcare diagnosis. Conventional DNNs are implemented using cloud computing, where a large amount of computing resource is available in a centrally-pooled manner. In order to achieve stronger data privacy, less response time and relaxed data transmission burden, deploying DNN functionality in a distributed manner at the edges of the network has become a very attractive proposition. However, DNN-learning on mobile devices that are at the edge of the network is very challenging due to conflicting requirements of large time and energy consumption, and limited on-device resources. In order to address this challenge, this project leverages low-rank tensors as a powerful mathematical tool for representing and compressing tensor-format data, to form a new family of ultra-low cost deep neural networks. This brings an order-of-magnitude reduction in time and energy consumption for deep neural network learning. Investigations in many areas of BigData research will benefit as well. This project involves graduate and undergraduate students, especially from underrepresented groups, through summer research experiences, and senior design projects to broaden the participation of computing. The outcomes of this project will be disseminated to the community in the format of technical publications, talks and tutorials in both academic institutions and industry.In order to remove the barriers of realizing real-time energy-efficient DNN-learning on the resource and energy-constrained embedded devices, this project considers innovations at three levels: 1) at theory level, it develops a novel redundancy-free matrix-vector multiplication scheme to reduce computational cost, including a new online update scheme for low-rank tensors to enable fast compressed data update; 2) at algorithm level, it develops low-rank tensor-based forward and backward propagation schemes to support low-cost accelerated inference and training, including catastrophic forgetting-resilient training scheme and training-aware compression scheme to improve the learning robustness and memory efficiency; and 3) at hardware design level, it proposes efficient hardware architecture that fully utilize the benefits provided by low-rank tensors to achieve improved hardware performance for on-device DNN inference and learning. Finally, the efficacy of the proposed research will be validated and evaluated, via software implementations on different DNN models in different target applications. A field-programmable gate array (FPGA)-based hardware prototype will also be developed.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.
深度神经网络(Deep neural network, DNN)是一种重要的人工智能技术,近年来在图像识别、机器翻译、自动驾驶汽车和医疗诊断等众多领域得到了广泛的应用。传统的深度神经网络是通过云计算实现的,在云计算中,大量的计算资源以集中的方式可用。为了实现更强的数据隐私、更短的响应时间和更轻松的数据传输负担,在网络边缘以分布式方式部署DNN功能已成为一个非常有吸引力的提议。然而,在处于网络边缘的移动设备上进行dnn学习是非常具有挑战性的,因为它需要大量的时间和能量消耗,并且设备上的资源有限。为了应对这一挑战,该项目利用低秩张量作为一种强大的数学工具来表示和压缩张量格式的数据,形成一个新的超低成本深度神经网络家族。这为深度神经网络学习带来了时间和能量消耗的数量级减少。大数据研究的许多领域的调查也将从中受益。这个项目涉及研究生和本科生,特别是来自代表性不足的群体,通过暑期研究经验和高级设计项目来扩大计算机的参与。该项目的成果将在学术机构和工业界以技术出版物、讲座和教程的形式向社会传播。为了消除在资源和能源受限的嵌入式设备上实现实时节能dnn学习的障碍,本项目考虑了三个层面的创新:1)在理论层面,开发了一种新的无冗余矩阵向量乘法方案来降低计算成本,包括一种新的低秩张量在线更新方案,以实现快速压缩数据更新;2)在算法层面,开发了基于低秩张量的正向和反向传播方案,支持低成本的加速推理和训练,包括灾难性遗忘弹性训练方案和训练感知压缩方案,提高学习鲁棒性和记忆效率;3)在硬件设计层面,提出了高效的硬件架构,充分利用低秩张量提供的优势,以提高设备上DNN推理和学习的硬件性能。最后,将通过不同目标应用中不同深度神经网络模型的软件实现来验证和评估所提出研究的有效性。一个基于现场可编程门阵列(FPGA)的硬件原型也将被开发。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(8)
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 Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design
通过软硬件协同设计在资源有限的硬件设备上构建高效的实时目标检测框架
- DOI:10.1109/asap52443.2021.00020
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Liu, Mingshuo;Luo, Shiyi;Han, Kevin;Yuan, Bo;DeMara, Ronald F.;Bai, Yu
- 通讯作者:Bai, Yu
Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition With Hierarchical Tucker Tensor Decomposition
- DOI:10.1109/tc.2022.3212642
- 发表时间:2022-12
- 期刊:
- 影响因子:3.7
- 作者:Yu Gong;Miao Yin;Lingyi Huang;Chunhua Deng;Bo Yuan
- 通讯作者:Yu Gong;Miao Yin;Lingyi Huang;Chunhua Deng;Bo Yuan
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
- DOI:10.1609/aaai.v37i9.26244
- 发表时间:2023-01
- 期刊:
- 影响因子:0
- 作者:Jinqi Xiao;Chengming Zhang;Yu Gong;Miao Yin;Yang Sui;Lizhi Xiang;Dingwen Tao;Bo Yuan
- 通讯作者:Jinqi Xiao;Chengming Zhang;Yu Gong;Miao Yin;Yang Sui;Lizhi Xiang;Dingwen Tao;Bo Yuan
TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition
- DOI:10.1145/3572848.3577478
- 发表时间:2022-11
- 期刊:
- 影响因子:0
- 作者:Lizhi Xiang;Miao Yin;Chengming Zhang;Aravind Sukumaran-Rajam;P. Sadayappan;Bo Yuan;Dingwen Tao
- 通讯作者:Lizhi Xiang;Miao Yin;Chengming Zhang;Aravind Sukumaran-Rajam;P. Sadayappan;Bo Yuan;Dingwen Tao
An Efficient Video Prediction Recurrent Network using Focal Loss and Decomposed Tensor Train for Imbalance Dataset
- DOI:10.1145/3453688.3461748
- 发表时间:2021-06
- 期刊:
- 影响因子:0
- 作者:Mingshuo Liu;Kevin Han;Shiying Luo;Mingze Pan;M. Hossain;Bo Yuan;R. Demara;Y. Bai
- 通讯作者:Mingshuo Liu;Kevin Han;Shiying Luo;Mingze Pan;M. Hossain;Bo Yuan;R. Demara;Y. Bai
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Bo Yuan其他文献
Capturing tensile size-dependency in polymer nanofiber elasticity
捕获聚合物纳米纤维弹性的拉伸尺寸依赖性
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:3.9
- 作者:
Bo Yuan;Jun Wang;Ray P.S. Han - 通讯作者:
Ray P.S. Han
Towards Solving TSPN with Arbitrary Neighborhoods: A Hybrid Solution
解决具有任意邻域的 TSPN:混合解决方案
- DOI:
10.1007/978-3-319-51691-2_18 - 发表时间:
2017 - 期刊:
- 影响因子:0
- 作者:
Bo Yuan;Tiantian Zhang - 通讯作者:
Tiantian Zhang
Streptomyces phytohabitans sp. nov., a novel endophytic actinomycete isolated from medicinal plant Curcuma phaeocaulis
植物栖息链霉菌 sp.
- DOI:
10.1007/s10482-012-9737-8 - 发表时间:
2012-04 - 期刊:
- 影响因子:2.6
- 作者:
Guang-Kai Bian;Sheng Qin;Bo Yuan;Yue-Ji Zhang;Ke Xing;Xiu-Yun Ju;Wen-Jun Li;Ji-Hong Jiang - 通讯作者:
Ji-Hong Jiang
Comparison of fibrogenesis caused by dermal and adipose tissue injury in an experimental model
实验模型中真皮和脂肪组织损伤引起的纤维形成的比较
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:2.9
- 作者:
Bo Yuan;Xiqiao Wang;Zhiyong Wang;Jun Wei;C. Qing;Shuliang Lu - 通讯作者:
Shuliang Lu
The effects of awe on interpersonal forgiveness: the mediating role of small-self
敬畏对人际宽恕的影响:小自我的中介作用
- DOI:
10.3389/fpsyg.2024.1336068 - 发表时间:
2024 - 期刊:
- 影响因子:3.8
- 作者:
Suxia Liao;Yichang Liu;Bo Yuan - 通讯作者:
Bo Yuan
Bo Yuan的其他文献
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{{ truncateString('Bo Yuan', 18)}}的其他基金
CAREER: SHF: Chimp: Algorithm-Hardware-Automation Co-Design Exploration of Real-Time Energy-Efficient Motion Planning
职业:SHF:黑猩猩:实时节能运动规划的算法-硬件-自动化协同设计探索
- 批准号:
2239945 - 财政年份:2023
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
Renewal: Preparing Crosscutting Cybersecurity Scholars
更新:培养跨领域网络安全学者
- 批准号:
1922169 - 财政年份:2019
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
SHF: Small: Collaborative Research: LDPD-Net: A Framework for Accelerated Architectures for Low-Density Permuted-Diagonal Deep Neural Networks
SHF:小型:协作研究:LDPD-Net:低密度置换对角深度神经网络加速架构框架
- 批准号:
1854737 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
AitF: Collaborative Research: A Framework of Simultaneous Acceleration and Storage Reduction on Deep Neural Networks Using Structured Matrices
AitF:协作研究:使用结构化矩阵的深度神经网络同时加速和存储减少的框架
- 批准号:
1854742 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
SHF: Small: Collaborative Research: LDPD-Net: A Framework for Accelerated Architectures for Low-Density Permuted-Diagonal Deep Neural Networks
SHF:小型:协作研究:LDPD-Net:低密度置换对角深度神经网络加速架构框架
- 批准号:
1815699 - 财政年份:2018
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
AitF: Collaborative Research: A Framework of Simultaneous Acceleration and Storage Reduction on Deep Neural Networks Using Structured Matrices
AitF:协作研究:使用结构化矩阵的深度神经网络同时加速和存储减少的框架
- 批准号:
1733834 - 财政年份:2017
- 资助金额:
$ 40万 - 项目类别:
Standard Grant
SFS: Preparing Crosscutting Cybersecurity Scholars
SFS:培养跨领域网络安全学者
- 批准号:
1433736 - 财政年份:2015
- 资助金额:
$ 40万 - 项目类别:
Continuing Grant
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- 批准号:10774081
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