EAGER: Language and Architecture Design for Approximation at Different Granularities

IT行业的经济生态系统主要依赖于通过利用通用处理的持续性能和效率改进来持续提供新的服务和设备。然而，随着我们进入黑暗的硅时代，晶体管扩展带来的好处正在减少，目前的处理器设计范例显著低于传统的性能改进节奏。这些缺陷可能会极大地削弱该行业持续提供新能力的能力，破坏其经济生态系统的支柱。必须彻底改变传统方法，才能为广泛的应用程序提供巨大的效率和性能收益。一个这样的背离是通用的近似计算，它接受计算中的误差，并放松了“近乎完美”精度的传统抽象。近似计算利用了大量新兴应用程序的固有容错能力。这些应用程序跨越广泛的领域，包括视觉、大数据分析、机器学习、传感器处理、网络物理系统、多媒体和网络搜索。对于这些不同的应用程序领域，及时且至关重要的是提供体系结构机制和编程抽象，以实现以结果质量换取性能和效率的收益。该项目旨在提供体系结构机制和编程语言构造，使近似计算有效地适用于广泛的应用领域。能源效率是IT行业面临的最大挑战。为了保持国家在IT行业的经济领先地位，开发像我们这样的解决方案至关重要，这些解决方案可以显著提高效率和性能。其中许多技术允许近似跨越硬件和软件的边界。因此，培养一支不仅对硬件和软件了如指掌，而且能够跨越硬件和软件界限进行创新的员工队伍至关重要。该项目通过为近似计算生成基准、工具和通用基础设施，为此类研究和教育提供了基础。这些文物将公开提供，并将被整合到课程中。

The IT industry's economic ecosystem mostly relies on continuously delivering new services and devices by exploiting continuous performance and efficiency improvements in general-purpose processing. However, as we enter the dark silicon era, the benefits from transistor scaling are diminishing and the current paradigm of processor design significantly falls short of the traditional cadence of performance improvements. These shortcomings can drastically curtail the industry's ability to continuously deliver new capabilities, breaking the backbone of its economic ecosystem. Radical departures from conventional approaches are necessary to provide large efficiency and performance gains for a wide range of applications. One such departure is general-purpose approximate computing that accepts error in computation and relaxes the traditional abstraction of "near-perfect" accuracy. Approximate computing leverages the inherent error tolerance of a large body of emerging applications. These applications span a wide range of domains including vision, big data analytics, machine learning, sensor processing, cyber-physical systems, multimedia, and web search. For these diverse domains of applications, it is timely and crucial to provide architectural mechanisms and programming abstractions that enable trading quality of results for gains in both performance and efficiency. This project aims to provide both architectural mechanisms and programming language constructs that make approximate computing effectively applicable to a wide range of domains of applications. Energy efficiency is the IT industry's biggest challenge. To maintain the nation's economic leadership in the IT industry, it is vital to develop solutions such as ours that provide significant gains in efficiency and performance. Many of these techniques allow approximation to permeate across the boundaries of hardware and software. Thus, it is essential to educate a workforce that not only deeply understands hardware and software but also can innovate across the boundaries of the two. This project provides a foundation for such research and education by producing benchmarks, tools, and general infrastructure for approximate computing. These artifacts will be made publicly available and will be integrated into the curriculum.