CAREER: Effectuating Hardware-Accelerated Systems with Domain-Specific Primitives

职业:使用特定于领域的原语实现硬件加速系统

基本信息

  • 批准号:
    2045974
  • 负责人:
  • 金额:
    $ 54.44万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-03-01 至 2026-02-28
  • 项目状态:
    未结题

项目摘要

With enormous volumes and vast varieties of data being produced and consumed every second, having computer systems that can process large volumes and diverse sets of data in unprecedented efficiency is imperative for computer architects and system designers to keep up with this data deluge. Custom hardware has long been a solution to run a specific application or a set of applications at exceptional performance and energy efficiency compared to general purpose platforms. However, the development and deployment of custom hardware has been time-consuming and expensive. Moreover, the toolchain, programming, and usage of custom hardware are complex and obtuse, reducing its accessibility. Thus, there is a need to innovate and drastically reduce the development and deployment costs of custom hardware while making these accelerated systems accessible to not only computer system designers but also non-hardware savvy scientists to expedite their research analyses and advance knowledge in their perspective fields. This research develops a novel execution paradigm to democratize hardware accelerated system designs. This research approach leverages application-specific knowledge to guide the underlying hardware design and compose highly efficient, programmable, portable, and easy to use custom hardware systems that are orders of magnitude faster and more power efficient. This research promotes the progress of computer science and computer system designs and serves as an education tool in the computer research community to advance knowledge in hardware acceleration abstractions, concepts, and effectuations. In addition, this research can be applied to several example domains to solve open problems in the interdisciplinary area of computer architecture and healthcare such as cancer research and drug discovery to improve national health and bring direct impacts to human flourishing. This project explores how to create highly efficient hardware-accelerated systems for compute- and memory-intensive applications that process enormous volumes of data. The key idea is to develop a new execution paradigm, that decomposes algorithms into coarse-grained operators instead of traditional execution work units such as instructions, that describes how and what portions of applications can be accelerated and executed on a hardware-accelerated system. Such execution work units are called Domain-Specific Primitives (DSPs). The vision is to raise the level of abstraction when designing hardware accelerators by examining domain-specific, already-defined software datatypes and constructs and thereby creating hardware execution units that closely match software datatype-specific method calls, the starting point of DSPs. DSPs can be used by software compilers, programmers, as well as hardware development designers to compose, program, develop, and optimize domain-specific hardware-accelerated systems in a systematic fashion. This project first develops the fundamental concepts and abstractions of DSP designs. This project then explores the productivity, resultant system performance and energy efficiency, portability, and the ease of use of DSP-composed accelerated systems. The result is a transformative realization in highly performant, highly efficient, and highly productive development of hardware-accelerated systems that are easy to use. This research makes a significant contribution towards teaching acceleration concepts via various education and outreach programs. In addition, the research will advance computer systems and healthcare research and enable industry technology transfer.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.
由于每一秒钟都要产生和消耗大量和种类繁多的数据,对于计算机架构师和系统设计师来说,拥有能够以前所未有的效率处理大量和各种数据集的计算机系统是必要的,以跟上这种数据洪流。与通用平台相比,定制硬件长期以来一直是运行特定应用程序或一组应用程序的解决方案,具有卓越的性能和能源效率。然而,定制硬件的开发和部署既耗时又昂贵。此外,定制硬件的工具链、编程和使用是复杂和迟钝的,降低了它的可访问性。因此,有必要进行创新,大幅降低定制硬件的开发和部署成本,同时使这些加速系统不仅可供计算机系统设计师使用,而且也可供非硬件精通的科学家使用,以加快他们的研究分析,并在他们的观点领域推进知识。本研究开发了一种新的执行范式,使硬件加速系统设计大众化。这种研究方法利用特定于应用程序的知识来指导底层硬件设计,并组成高效、可编程、便携和易于使用的定制硬件系统,这些系统的速度更快,效率更高。这项研究促进了计算机科学和计算机系统设计的进步,并作为计算机研究界的教育工具,促进了硬件加速抽象、概念和效果方面的知识。此外,本研究还可以应用于多个示例领域,解决癌症研究、药物发现等计算机体系结构与医疗保健交叉领域的开放性问题,从而提高国民健康水平,直接影响人类的繁荣。本项目探索如何为处理大量数据的计算和内存密集型应用程序创建高效的硬件加速系统。关键思想是开发一种新的执行范式,将算法分解为粗粒度的运算符,而不是传统的执行工作单元(如指令),它描述了如何以及哪些部分的应用程序可以在硬件加速系统上加速和执行。这样的执行工作单元称为领域特定原语(Domain-Specific Primitives, dsp)。在设计硬件加速器时,通过检查领域特定的、已经定义的软件数据类型和构造,从而创建与软件数据类型特定的方法调用(dsp的起点)紧密匹配的硬件执行单元,从而提高抽象级别。dsp可以被软件编译器、程序员以及硬件开发设计人员使用,以系统的方式组合、编程、开发和优化特定领域的硬件加速系统。本课题首先发展了DSP设计的基本概念和抽象。这个项目然后探索生产力,由此产生的系统性能和能源效率,便携性,和易于使用的dsp组成的加速系统。其结果是在易于使用的硬件加速系统的高性能、高效率和高生产力开发中实现了变革性的实现。本研究透过各种教育及外展计划,对教学加速概念作出重大贡献。此外,这项研究将促进计算机系统和医疗保健研究,并使行业技术转移成为可能。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(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
ProSE: the architecture and design of a protein discovery engine
PyTFHE: An End-to-End Compilation and Execution Framework for Fully Homomorphic Encryption Applications
SNS's not a synthesizer: a deep-learning-based synthesis predictor
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Effectuating Evidence-based Transformative Pedagogical Approaches in STEM Foundational Courses at AAMU
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  • 批准号:
    1347749
  • 财政年份:
    2014
  • 资助金额:
    $ 54.44万
  • 项目类别:
    Standard Grant
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