Approximate Computing for Low-Power Many-Core Processors

低功耗众核处理器的近似计算

• 批准号: RGPIN-2018-03854
• 负责人: Baniasadi, Amirali
• 金额: $ 1.68万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=644577
• 关键词: Approximate; Computing; Low; Power; Many

Developing more efficient computing systems is a critical goal of the high-tech industry. To this end, designers have suggested hardware (i.e., energy efficient components and systems) and software methods (compiler solutions for generating energy efficient code).*** Developing efficient computing systems not only has resulted in environmental benefits, but has also helped reducing production cost and complexity and achieving longer battery lives. Recent studies however, have shown that the same level of efficiency is not sustainable and that further achievements in developing low-power solutions face difficulties imposed not only by technology limitations but also application requirements.***Traditionally, applications running on computing systems have required intact accuracy. ***In recent years many studies have analyzed computing at the application level and have shown that many applications can tolerate an acceptable level of inaccuracy and approximation in their computations.***Approximate computing builds on this observation and is a promising solution to maintaining energy efficiency in the future.*** Conventional low-power design explored a two dimensional space where trade-offs between power and performance were explored with the goal of maximising energy savings while maintaining performance. With the inclusion of approximation, this design space has been extended to a three dimensional one where accuracy can also be traded for higher efficiency. This extension motivates us to pursue new opportunities and further optimizations.*** This research will deliver new ways to explore and build approximate-aware systems relying on a less aggressive approach to computing but still capable of delivering acceptable results.***We propose a deep analysis of General Purpose Graphic Processing Units (GPGPUs) and***developing both hardware and software solutions to reduce energy consumption and improve efficiency while maintaining accuracy within acceptable limits. While our hardware solutions will focus on designing new and efficient Graphic Processing Units, our software solutions will use a compiler based approach to identify opportunities to achieve low-complexity computing.*** Our past experience with hardware and software optimizations, application behaviour analysis and our familiarity with the tools available provide us with the required skills to develop low-power approximate-aware systems.***Our proposed research makes important contributions to the Canadian society. First, the proposed program aims at developing "greener" computing infrastructures where power hungry GPGPUs are replaced with low-power alternatives. Second, the resulting knowledge belongs to an area critical to Canada's future leadership in advanced technologies. Many Canadian companies can benefit from the findings of the proposed research and the resulting HQP training.

开发更高效的计算系统是高科技行业的一个关键目标。为此，设计人员提出了硬件（即节能组件和系统）和软件方法（用于生成节能代码的编译器解决方案）的建议。*** 开发高效的计算系统不仅带来了环境效益，而且还有助于减少能源消耗生产成本和复杂性以及实现更长的电池寿命。然而，最近的研究表明，相同的效率水平是不可持续的，并且在开发低功耗解决方案方面取得进一步的成就不仅面临技术限制，而且还面临应用要求带来的困难。***传统上，在计算系统上运行的应用程序需要完好无损的准确性。 ***近年来，许多研究分析了应用程序级别的计算，并表明许多应用程序可以容忍计算中可接受的不准确性和近似水平。***近似计算建立在这一观察的基础上，是维护*** 传统的低功耗设计探索了一个二维空间，在其中探索了功耗和性能之间的权衡，目标是在保持性能的同时最大限度地节省能源。通过包含近似值，该设计空间已扩展到三维空间，其中精度也可以换取更高的效率。这一扩展激励我们寻求新的机会和进一步的优化。***这项研究将提供新的方法来探索和构建近似感知系统，依赖于不太激进的计算方法，但仍然能够提供可接受的结果。***我们建议深入分析通用图形处理单元 (GPGPU) 并***开发硬件和软件解决方案，以降低能耗并提高效率，同时将精度保持在可接受的范围内。虽然我们的硬件解决方案将专注于设计新的高效图形处理单元，但我们的软件解决方案将使用基于编译器的方法来识别实现低复杂性计算的机会。*** 我们过去在硬件和软件优化、应用程序行为分析和我们对可用工具的熟悉为我们提供了开发低功耗近似感知系统所需的技能。***我们提出的研究为加拿大社会做出了重要贡献。首先，拟议的计划旨在开发“更绿色”的计算基础设施，其中耗电的 GPGPU 被低功耗替代品取代。其次，由此产生的知识属于对加拿大未来先进技术领导地位至关重要的领域。许多加拿大公司可以从拟议研究的结果以及由此产生的 HQP 培训中受益。