Collaborative Research: CCRI: New: A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning

协作研究：CCRI：新：可扩展的硬件和软件环境支持安全的多方学习

• 批准号: 2213701
• 负责人: Heng Huang
• 金额: $ 48万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213701&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; New; Scalable

Advances in machine learning have made a major impact on many real-world applications over the past decade, and have achieved scientific and engineering breakthroughs across many disciplines. A new era of collaborative learning is emerging as part of the next phase of ubiquitous computing, wherein researchers at different sites will work together to correlate the disparate data they have separately acquired and eventually create a sophisticated decision-making model. It is thus imperative to establish a platform to support collaborative, multi-party data analysis, through which the participating parties can share their data with each other with different degrees of privacy control. The participants can compute with each other's data, by either directly sharing data with the server or only sharing their model parameters with the server to collaboratively derive a solution with other parties. To make such an environment available to the community, this project establishes a scalable and trusted hardware and software environment, termed Bridge, to support a general form of collaborative machine learning. The Bridge platform enables scalable multi-party learning and data analysis in a variety of forms, in both centralized and decentralized settings, with security and privacy guarantees. The project's novelties are to synergistically design and integrate both hardware and software innovation as well as a suite of security and privacy mechanisms and tools to support various types of multi-party machine learning. The project's impacts are to enable collaborative research efforts in diverse communities of CISE researchers pursuing focused research agendas in computer and information science and engineering, and generate enormous social and economic benefits to individuals and organizations. The minority students and under-served populations will be engaged in research activities to create an inclusive environment where everyone contributes to and benefits from cutting-edge scientific research.The Bridge platform will develop a unified hardware and software infrastructure to achieve hardware and software co-design for multi-party learning. An algorithmic software infrastructure is designed to support distributed, federated, and multi-modal model learning and sharing. The Bridge platform integrates cryptographic (secure multi-party computation) and noise-based methods (differential privacy) to provide privacy across the entire process from data collection to output. The Bridge platform provides a set of tools on integrated data access, AutoML, team creation, machine learning model vulnerability evaluation, and heterogeneous feature embeddings to support flexible user applications. The Bridge platform ensures the scalability in the number of tasks, the number of users, and heterogeneity of data types by developing advanced techniques to improve asynchronous model updates, communication efficiency, fast convergence, and vertical data partition. The Bridge platform builds a collaborative learning community and accelerates many new research areas in the core Computer and Information Science and Engineering (CISE), such as advanced machine learning and data science, data privacy and trustworthy AI, convergent research among hardware, software and machine learning, and intelligent internet of things.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.

在过去的十年中，机器学习的进步对许多现实世界的应用产生了重大影响，并在许多学科中取得了科学和工程的突破。作为下一阶段无处不在的计算阶段的一部分，合作学习的新时代正在出现，其中不同站点的研究人员将共同努力，以将他们分别获取的不同数据相关联并最终创建一个复杂的决策模型。因此，必须建立一个支持协作，多方数据分析的平台，参与方可以通过不同程度的隐私控制。参与者可以通过与服务器直接共享数据，或者仅与服务器共享其模型参数以与其他方协作得出解决方案来计算彼此的数据。为了使社区可用的环境，该项目建立了一个可扩展且可信赖的硬件和软件环境，称为桥梁，以支持协作机器学习的一般形式。该桥平台可提供可扩展的多方学习和数据分析，并具有各种形式的集中和分散设置，并提供安全性和隐私保证。该项目的新颖性是协同设计和整合硬件和软件创新，以及一套安全性和隐私机制和工具，以支持各种类型的多方机器学习。该项目的影响是在CISE研究人员的各个社区中实现合作研究工作，从而追求计算机，信息科学和工程学的重点研究议程，并为个人和组织带来巨大的社会和经济利益。少数族裔学生和服务不足的人群将参与研究活动，以创建一个包容性的环境，每个人都为尖端的科学研究贡献和利益。桥平台将开发统一的硬件和软件基础架构，以实现多方学习的硬件和软件共同设计。算法软件基础架构旨在支持分布式，联合和多模式模型学习和共享。桥平台集成了密码学（安全的多方计算）和基于噪声的方法（差别隐私），以在整个过程中提供从数据收集到输出的整个过程的隐私。该桥平台提供了一组集成数据访问，汽车，团队创建，机器学习模型漏洞评估和异质功能嵌入的工具，以支持灵活的用户应用程序。桥平台可确保通过开发高级技术来改善异步模型更新，通信效率，快速收敛和垂直数据分区的高级技术来确保任务数量，用户数量以及数据类型的异质性。 The Bridge platform builds a collaborative learning community and accelerates many new research areas in the core Computer and Information Science and Engineering (CISE), such as advanced machine learning and data science, data privacy and trustworthy AI, convergent research among hardware, software and machine learning, and intelligent internet of things.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.

项目成果

Challenges and Opportunities to Enable Large-Scale Computing via Heterogeneous Chiplets

• DOI: 10.1109/asp-dac58780.2024.10473961
• 发表时间: 2023-11
• 期刊: 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC)
• 作者: Zhuoping Yang;Shixin Ji;Xingzhen Chen;Jinming Zhuang;Weifeng Zhang;Dharmesh Jani;Peipei Zhou

SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration

• DOI: 10.1145/3626202.3637569
• 发表时间: 2024-01
• 期刊: Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
• 作者: Jinming Zhuang;Zhuoping Yang;Shixin Ji;Heng Huang;Alex K. Jones;Jingtong Hu;Yiyu Shi;Peipei Zhou

AIM: Accelerating Arbitrary-Precision Integer Multiplication on Heterogeneous Reconfigurable Computing Platform Versal ACAP

目的：在异构可重构计算平台 Versal ACAP 上加速任意精度整数乘法

• DOI: 10.1109/iccad57390.2023.10323754
• 发表时间: 2023
• 期刊: IEEE
• 作者: Yang, Zhuoping;Zhuang, Jinming;Yin, Jiaqi;Yu, Cunxi;Jones, Alex K.;Zhou, Peipei
• 通讯作者: Zhou, Peipei

CHARM: Composing Heterogeneous AcceleRators for Matrix Multiply on Versal ACAP Architecture

CHARM：在 Versal ACAP 架构上组合用于矩阵乘法的异构加速器

• DOI: 10.1145/3543622.3573210
• 发表时间: 2023
• 期刊: Proceedings of the 2023 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
• 作者: Zhuang, Jinming;Lau, Jason;Ye, Hanchen;Yang, Zhuoping;Du, Yubo;Lo, Jack;Denolf, Kristof;Neuendorffer, Stephen;Jones, Alex;Hu, Jingtong
• 通讯作者: Hu, Jingtong

High Performance, Low Power Matrix Multiply Design on ACAP: from Architecture, Design Challenges and DSE Perspectives

ACAP 上的高性能、低功耗矩阵乘法设计：来自架构、设计挑战和 DSE 角度

• DOI: 10.1109/dac56929.2023.10247981
• 发表时间: 2023
• 期刊: 2023 60th ACM/IEEE Design Automation Conference (DAC
• 作者: Zhuang, Jinming;Yang, Zhuoping;Zhou, Peipei
• 通讯作者: Zhou, Peipei