SHF: Small: Collaborative Research: Integrated Framework for System-Level Approximate Computing

SHF：小型：协作研究：系统级近似计算的集成框架

• 批准号: 1812467
• 负责人: Fabrizio Lombardi
• 金额: $ 26.2万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812467&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Integrated

Nanocomputing is encountering fundamental challenges with respect to performance and power consumption; it requires different computational paradigms that exploit specific features in the targeted set of applications as well as an integrated framework for assessing the interactions between hardware and the processing algorithms (software). Approximate (inexact) computing has been advocated as a novel approach for nanocomputing design. Approximate computing generates results that are good enough rather than always fully accurate and correct outputs. Recent advances at circuit level have shown that there is an urgent need to investigate and enable at system-level the flexible utilization, improvement and close monitoring of approximate resources; this allows the efficient and integrated interaction of algorithms and hardware to meet the multiple and often conflicting figures of merit of high performance, lower power consumption and reduced inaccuracy. The goal of this project is to develop approximate computing systems that are capable of adjusting performance by exploiting relationships between hardware and software (referred to as intra-level) in different applications such as cognitive processing, DSP, big data and scientific processing for which data can be adaptively utilized and manipulated. This project is an organized effort that combines recent advances in technology with architectural enhancements into an integrated framework for approximate computing that will tackle the critical challenges of emerging computer designs in a comprehensive manner. This framework consists of new functional and computational primitives of hardware resources and related algorithms to allow an evaluation at system-level to meet the desired metrics for approximate computing. Intra-layer relationships such as number representation (such as floating point and logarithm) and accuracy by employing dynamic approximation schemes and data remediation for both communication and computing are also analyzed.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.

纳米计算在性能和功耗方面遇到了根本性的挑战;它需要不同的计算范式，这些范式利用目标应用程序中的特定功能，以及用于评估硬件和处理算法（软件）之间的交互的集成框架。近似（不精确）计算已被提倡作为一种新的方法，纳米计算设计。近似计算产生足够好的结果，而不是总是完全准确和正确的输出。电路级的最新进展表明，迫切需要在系统级研究并实现对近似资源的灵活利用、改进和密切监控;这允许算法和硬件的高效和集成交互，以满足高性能、低功耗和降低不准确性的多个且经常相互冲突的优点。 该项目的目标是开发近似计算系统，该系统能够通过利用不同应用程序（如认知处理，DSP，大数据和科学处理）中硬件和软件（称为内部级别）之间的关系来调整性能，其中数据可以自适应地利用和操纵。这个项目是一个有组织的努力，结合了最新的技术进步与架构增强到一个综合框架的近似计算，将解决新兴的计算机设计的关键挑战，在一个全面的方式。该框架由硬件资源和相关算法的新功能和计算原语组成，以允许在系统级进行评估，以满足近似计算所需的指标。层内的关系，如数字表示（如浮点数和对数）和精度，采用动态近似方案和数据补救的通信和计算也进行了分析。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

A Hardware/Software Co-design Method for Approximate Semi-Supervised K-Means Clustering

• DOI: 10.1109/isvlsi.2018.00110
• 发表时间: 2018-07
• 期刊: 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)
• 作者: Pengfei Huang;Chenghua Wang;Ruizhe Ma;Weiqiang Liu;F. Lombardi

A Deterministic Low-Complexity Approximate (Multiplier-Less) Technique for DCT Computation

• DOI: 10.1109/tcsi.2019.2902415
• 发表时间: 2019-08-01
• 期刊: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
• 影响因子: 5.1
• 作者: Huang, Junqi;Kumar, T. Nandha;Lombardi, Fabrizio
• 通讯作者: Lombardi, Fabrizio

A Probabilistic Error Model and Framework for Approximate Booth Multipliers

• DOI: 10.1145/3232195.3232200
• 发表时间: 2018-07
• 期刊: 2018 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)
• 作者: Yuying Zhu;Weiqiang Liu;Jie Han;F. Lombardi

Efficient Implementations of Reduced Precision Redundancy (RPR) Multiply and Accumulate (MAC)

降低精度冗余 (RPR) 乘法和累加 (MAC) 的高效实现

• DOI: 10.1109/tc.2018.2885044
• 发表时间: 2019
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Chen, Ke;Chen, Linbin;Reviriego, Pedro;Lombardi, Fabrizio
• 通讯作者: Lombardi, Fabrizio