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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

基本信息

批准号：
1812495
负责人：
Ahmed Louri
金额：
$ 22.5万
依托单位：
George Washington 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 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812495&HistoricalAwards=false
关键词：
SHF Small Collaborative Research Integrated

项目摘要

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面临着根本性的挑战；它需要利用目标应用程序集中的特定功能的不同计算范式，以及用于评估硬件和处理算法（软件）之间交互的集成框架。近似（不精确）计算被认为是纳米计算设计的一种新方法。近似计算产生的结果足够好，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，迫切需要在系统级调查和实现对近似资源的灵活利用、改进和密切监测；这允许算法和硬件的高效和集成交互，以满足高性能，低功耗和减少不准确性的多重且经常相互冲突的数字。该项目的目标是开发近似计算系统，该系统能够通过利用认知处理，DSP，大数据和科学处理等不同应用中硬件和软件之间的关系（称为内部级）来调整性能，从而可以自适应地利用和操纵数据。该项目是一项有组织的工作，它将最近的技术进步与体系结构增强结合到近似计算的集成框架中，该框架将以全面的方式解决新兴计算机设计的关键挑战。该框架由硬件资源和相关算法的新功能和计算原语组成，允许在系统级进行评估，以满足近似计算所需的度量。还分析了层内关系，如数字表示（如浮点数和对数）和采用动态近似方案的精度以及通信和计算的数据修复。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（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Technique for Approximate Communication in Network-on-Chips for Image Classification

DOI：
10.1109/tetc.2022.3162165
发表时间：
2021-08
期刊：
IEEE Transactions on Emerging Topics in Computing
影响因子：
5.9
作者：
Yuechen Chen;Shanshan Liu;Fabrizio Lombardi;A. Louri
通讯作者：
Yuechen Chen;Shanshan Liu;Fabrizio Lombardi;A. Louri

An Approximate Communication Framework for Network-on-Chips

DOI：
10.1109/tpds.2020.2968068
发表时间：
2020-06
期刊：
IEEE Transactions on Parallel and Distributed Systems
影响因子：
5.3
作者：
Yuechen Chen;A. Louri
通讯作者：
Yuechen Chen;A. Louri

Learning-Based Quality Management for Approximate Communication in Network-on-Chips

DOI：
10.1109/tcad.2020.3012235
发表时间：
2020-11
期刊：
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
影响因子：
2.9
作者：
Yuechen Chen;A. Louri
通讯作者：
Yuechen Chen;A. Louri

A High-Performance and Energy-Efficient FIR Adaptive Filter Using Approximate Distributed Arithmetic Circuits