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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752031
• 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) anddeveloping 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.

开发更有效的计算系统是高科技行业的关键目标。为此，设计师建议使用硬件（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）。开发有效的计算系统不仅带来了环境收益，而且还有助于降低生产成本和复杂性并实现更长的电池寿命。然而，最近的研究表明，相同水平的效率不是可持续的，并且在开发低功率解决方案的进一步成就面临着不仅受技术限制和应用程序要求施加的困难。在传统上，在计算系统上运行的应用程序需要完整的准确性。近年来，许多研究在应用级别分析了计算，并表明许多应用可以忍受其计算中可接受的不准确性和近似值的水平。对该观察结果的认可基于该观察结果，并且是维持未来能源效率的有希望的解决方案。传统的低功率设计探索了一个二维空间，在该空间中，探索了功率和性能之间的权衡，以最大程度地节省能源，同时保持性能。随着近似值的包含，该设计空间已扩展到三维的设计空间，在该空间中，精度也可以以提高效率进行交易。这种扩展促使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依靠不太积极的计算方法，但仍然能够提供可接受的结果。我们对通用图形处理单元（GPGPU）进行深入分析，并开发硬件和软件解决方案，以降低能源消耗并提高可接受限制的准确性。虽然我们的硬件解决方案将着重于设计新的，有效的图形处理单元，但我们的软件解决方案将使用基于编译器的方法来确定实现低复杂性计算的机会。我们过去在硬件和软件优化的经验，应用程序行为分析以及我们对可用工具的熟悉度为我们提供了开发低功率大致了解系统的所需技能。我们拟议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“绿色”计算基础架构，其中饥饿的GPGPU被低功率替代品取代。其次，由此产生的知识属于加拿大未来在高级技术领导的领域至关重要的领域。许多加拿大公司可以从拟议的研究的发现和由此产生的HQP培训中受益。