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

纳米计算在性能和功耗方面遇到了根本性的挑战；它需要不同的计算模式来利用目标应用程序集中的特定功能，以及评估硬件和处理算法(软件)之间的相互作用的综合框架。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，迫切需要研究和实现系统级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、低功耗和减少误差的多个且往往相互冲突的优值。该项目的目标是开发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的硬件和软件之间的关系(称为内部级别)来调整性能的近似计算系统，其中数据可以被自适应地利用和处理。该项目是一项有组织的努力，将最近的技术进步与结构改进结合起来，形成一个综合的近似计算框架，将以全面的方式应对新兴计算机设计的关键挑战。该框架包括新的硬件资源的函数和计算原语以及相关算法，以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(如浮点和对数)以及通过使用动态近似方案和数据补救进行通信和计算的准确性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.