SHF: Medium: Enabling Real-Time Federated Learning at the Edge: Algorithm and Circuit Co-Design

SHF：中：在边缘实现实时联合学习：算法和电路协同设计

• 批准号: 1955450
• 负责人: Borivoje Nikolic
• 金额: $ 100万
• 依托单位: University of California-Berkeley
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955450&HistoricalAwards=false
• 关键词: SHF; Medium; Enabling; Real; Time

Machine-learning applications are penetrating many new domains, and the increased concern for privacy is pushing training to the user devices, as opposed to being performed on large computers in the cloud. In the federated-learning paradigm, model training is performed on a large number of distributed devices with a user’s private local datasets, while the aggregate model is composed on the cloud. This approach poses a number of new challenges in both development and analysis of new algorithms and co-design of optimized hardware for efficient operation. For efficient deployment of federated learning, edge devices need to support many other aspects of interest for this program, among them on-device learning and incremental model updates with private data, often performed in real time. This project proposes to build an end-to-end, real-time federated-learning framework, ranging from algorithmic innovations, hardware-software co-design, and efficient hardware demonstrations in scaled technologies. Concurrently, the proposed education activities will enable the development of engineers and scientists whose expertise spans a broad range from algorithms to digital system implementation.The real-time machine federated-learning concept requires integration of theoretical algorithm aspects with their practical development. Theoretical aspects include the compression of model size, reduction in communication requirements and the assessment of performance. Practical aspects include efficient hardware for training and inference, randomized sketching with near-memory computation and hardware-aware neural network design. In particular, it aims to achieve: (1) A full demonstration of a scalable and energy-efficient real-time federated learning architecture suitable for deployment in various scenarios, (2) Experimental measurements via test chips and cloud-based FPGA simulation to validate the developed system models, (3) Release of the platform in the open source. The key products of this work integrate research and education and encompass scalable randomized sketching algorithms, energy- and cost-efficient machine-learning accelerators for on-device training, and fast-and-accurate hardware-modeling infrastructure for hardware-aware algorithm design.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.

机器学习应用正在渗透到许多新的领域，对隐私的日益关注正在将训练推向用户设备，而不是在云中的大型计算机上执行。在联合学习范式中，模型训练是在大量具有用户私有本地数据集的分布式设备上执行的，而聚合模型是在云上组成的。 这种方法在新算法的开发和分析以及优化硬件的协同设计方面提出了许多新的挑战。为了有效地部署联合学习，边缘设备需要支持该计划的许多其他方面，其中包括设备上学习和使用私有数据的增量模型更新，通常是真实的时间。该项目旨在构建一个端到端的实时联合学习框架，包括算法创新，硬件-软件协同设计以及规模化技术中的高效硬件演示。同时，拟议的教育活动将使工程师和科学家的专业知识跨越从算法到数字系统实现的广泛范围的发展。实时机器联合学习的概念需要理论算法方面与他们的实际开发相结合。 理论方面包括模型大小的压缩、通信需求的减少和性能的评估。实用方面包括用于训练和推理的高效硬件，具有近记忆计算的随机草图和硬件感知神经网络设计。具体而言，它旨在实现：（1）全面展示适用于各种场景部署的可扩展且节能的实时联邦学习架构，（2）通过测试芯片和基于云的FPGA仿真进行实验测量，以验证开发的系统模型，（3）以开源方式发布平台。这项工作的关键产品整合了研究和教育，包括可扩展的随机草图算法，用于设备上培训的节能和经济高效的机器学习加速器，以及用于硬件感知算法设计的快速准确的硬件建模基础设施。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Bellman Residual Orthogonalization for Offline Reinforcement Learning

• DOI: 10.48550/arxiv.2203.12786
• 发表时间: 2022-03
• 期刊: ArXiv
• 作者: A. Zanette;M. Wainwright

Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning

• DOI: 10.48550/arxiv.2206.00796
• 发表时间: 2022-06
• 作者: A. Zanette;M. Wainwright

Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning

• 发表时间: 2021-08
• 作者: A. Zanette;M. Wainwright;E. Brunskill

RoSÉ: A Hardware-Software Co-Simulation Infrastructure Enabling Pre-Silicon Full-Stack Robotics SoC Evaluation

• DOI: 10.1145/3579371.3589099
• 发表时间: 2023-06
• 期刊: Proceedings of the 50th Annual International Symposium on Computer Architecture
• 作者: Dima Nikiforov;Shengjun Chris Dong;Chengyi Zhang;Seah Kim;B. Nikolić;Y. Shao

A new similarity measure for covariate shift with applications to nonparametric regression

协变量平移的新相似性度量及其在非参数回归中的应用

• 发表时间: 2022
• 期刊: Proceedings of the International Conference on Machine Learning
• 作者: Pathak, Reese;Ma, Cong;Wainwright, Martin J.
• 通讯作者: Wainwright, Martin J.