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不断提供新的服务和设备。然而，随着我们进入暗硅时代，晶体管缩放的好处正在减少，当前的处理器设计范式明显低于传统的性能改进节奏。这些缺点可能会极大地削弱该行业不断提供新功能的能力，从而破坏其经济生态系统的支柱。为了为广泛的应用程序提供巨大的效率和性能收益，必须彻底改变传统方法。一种偏离是通用的近似计算，它接受计算中的误差，并放松了传统的“近乎完美”精度的抽象。近似计算利用了大量新兴应用程序固有的容错性。这些应用涵盖了广泛的领域，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。对于这些不同领域的应用程序，提供架构机制和编程抽象是及时且至关重要的，这些机制和编程抽象支持以结果质量换取性能和效率方面的收益。该项目旨在提供架构机制和编程语言结构，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行业的经济领导地位，开发像我们这样在效率和性能方面提供显著收益的解决方案至关重要。这些技术中的许多允许近似渗透到硬件和软件的边界。因此，教育员工不仅要深刻理解硬件和软件，而且要能够跨越两者的界限进行创新，这一点至关重要。该项目通过生成近似计算的基准、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.