CAREER: Enabling Verifiable Computation with Hardware Acceleration
CAREER: Enabling Verifiable Computation with Hardware Acceleration
批准号:
2145354
负责人:
Scott Beamer
金额:
$62.84万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-01-15 至 2026-12-31
中文摘要
该奖项部分由2021年美国救援计划法案(公法117-2)资助。随着计算变得越来越普遍,日常生活的一部分也直接受到它的影响。不幸的是,随着对机构信任度的下降,用户发现自己处于依赖于他们几乎不信任的计算的危险境地。幸运的是,可验证计算(VC),一种计算机科学中迅速兴起的能力,提供了一个候选的解决方案。用户可以通过检查计算产生的证明来验证是否执行了正确的计算,而不是要求用户盲目地相信他们的计算是由其他人正确计算的。风险投资的潜在应用越来越多,包括云卸载、金融、不可信芯片制造、区块链压缩和机器学习(ML)。然而,VC目前是不切实际的,因为生成证明的计算成本太高。硬件加速是VC天文计算需求的自然解决方案,但硬件设计成本的上升使专用硬件的开发复杂化。该项目通过研究新的硬件架构来加速VC和敏捷硬件设计技术来降低设计成本,从而解决了这两个问题。提供的加速将使更多的VC应用程序变得实用。VC的广泛使用使社会受益,因为用户将能够单独验证影响他们的计算的完整性。新的设计技术将在一个平易近人的课程中分享,其材料将是开源的,并且有很好的文档。此外,该项目还整合了教育方面的努力,如帮助代表性不足的大学生增强解决问题的能力,以及扩大攻读研究生的学生数量。VC研究领域的不成熟使硬件加速的开发在多种方式上复杂化,本项目旨在解决这些挑战。首先,风险投资是一个多样化和快速发展的领域,这使得单一的加速器架构不太可能支持所有的方法。幸运的是,许多方法重用操作和计算模式,因此本项目正在创建一个硬件生成器库,它可以作为构建所需VC方法的构建块。其次,由于VC是一种新事物,很少有架构方面的智慧,而且之前几乎没有针对VC的硬件加速工作。生成器的灵活性和可参数性使扩展的设计空间探索能够有效地发现高效的架构。第三,利用重用的所有机会需要拓扑、操作符和丰富数据类型的重要组合。该项目正在开发深度组合,以在不牺牲效率的情况下提高重用的抽象级别。为处理新的VC工作负载而开发的创新架构将为未来的VC应用研究提供支持。VC硬件加速器的开源构建模块库将帮助启动一个新的研究领域。创建一个新的研究领域,除了新的课程和外展活动,将有助于扩大和发展硬件设计社区。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。
英文摘要
This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2).As computation has become increasingly ubiquitous, so has the fraction of daily life directly impacted by it. Unfortunately, with declining trust in institutions, users find themselves in the perilous position of relying on computations they have little trust in. Fortunately, verifiable computation (VC), a rapidly emerging capability in computer science, provides a candidate solution. Instead of requiring users to blindly trust that their computations are correctly computed by others, users can verify that the correct computation was performed by inspecting a proof produced with the computation. There is a growing list of potential applications for VC including cloud offload, finance, untrusted chip fabrication, blockchain compression, and machine learning (ML). However, VC is currently impractical because generating the proofs is too computationally expensive. Hardware acceleration is a natural solution to VC's astronomical computational demands, but rising hardware-design costs complicate the development of specialized hardware. This project attacks both problems by investigating novel hardware architectures to accelerate VC and agile hardware-design techniques to reduce design costs. The acceleration provided will make many more VC applications practical. Enabling the widespread use of VC benefits society, as users will be able to individually verify the integrity of computations that affect them. The new design techniques will be shared in an approachable course whose materials will be open-sourced and well documented. In addition, the project integrates educational efforts such as helping underrepresented college students strengthen their problem-solving skills as well as broadening the pool of students pursuing graduate study.The immaturity of the VC research area complicates developing hardware acceleration in multiple ways, and this project is intended to solveee these challenges. First, VC is a diverse and rapidly moving field, which makes it unlikely that a single accelerator architecture will be able to support all approaches. Fortunately, many approaches reuse operations and computational patterns, so this project is creating a library of hardware generators that can serve as building blocks to construct the desired VC approaches. Second, since VC is so new, there is little architectural wisdom and nearly no prior work on hardware acceleration for VC. The flexibility and parameterizability of the generators enables expansive design space explorations to productively discover efficient architectures. Third, exploiting all of the opportunities for reuse requires non-trivial compositions of topologies, operators, and rich data types. The project is developing deep composition to raise the abstraction level of reuse without sacrificing efficiency. The innovative architectures developed to handle the novel VC workload will empower future VC application research. The open-source library of building blocks for VC hardware accelerators will help kickstart a new research area. Creating a new research area in addition to the new course and outreach activities, will help broaden and grow the hardware-design community.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.
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Collaborative Research: PPoSS: Planning: Extreme-scale Sparse Data Analytics
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批准号:2119154
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项目类别:Standard Grant
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资助金额:$5.0万
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财政年份:2021
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负责人:Scott Beamer
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依托单位:
海外基金