CAREER: Approximate Computing Systems for Future Teraflops Workloads

职业：未来 Teraflops 工作负载的近似计算系统

• 批准号: 1600896
• 负责人: Nam Sung Kim
• 金额: $ 9.12万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-21 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1600896&HistoricalAwards=false
• 关键词: CAREER; Approximate; Computing; Systems; Future

The performance of computers has improved tremendously in the past four decades, which has enabled innumerable applications that have major roles in our daily lives. However, without dramatic innovations in improving power efficiency of computing, the continued semiconductor device scaling alone will fail to provide sufficient performance for the future computing capabilities. For the emerging challenge, the proposed research investigates revolutionary computing paradigms to process, comprehend, and use abundant data in an extremely efficient way. The novelty of the approach lies in developing power- and performance-efficient computing systems with software support by exploiting the characteristics of future workloads that often use complex probabilistic mathematical models of physical phenomena. In such applications, approximate computing can often result in satisfactory outcomes. Meanwhile, it can dramatically decrease power consumption or increase performance by replacing complete logic functions with simplified circuits that mimic the functions for rough calculations. To extend the approximate computing concept to more general-purpose computing systems, the following holistic approaches are proposed: 1) intelligent microarchitectures to identify correctness-non-critical regions of code with compiler support; 2) approximate computing engines to execute such regions of code power and performance efficiently; 3) high-level morphic primitives to process a large fraction of workloads with orders-of-magnitude greater power and performance efficiency; and 4) flexible architectures to allow programmers and users to trade the quality of computing with the efficiency. The proposed research will have a specific and significant impact on the computer architecture, circuit, and compiler communities since it requires analysis of interesting and representative workloads; realization of state-of-the-art circuit, architecture, and compiler infrastructure; and invention of powerful and useful evaluation methodologies. Since most of the development and research work will be conducted by graduate students, both industry and academia will benefit from well-educated and trained employees as well as direct technology transfer when students graduate and begin employment elsewhere. Finally, the success of this research will tremendously benefit our ability to advance human?s collective knowledge in science, technology, business, medicine, and virtually every other field of human endeavor by allowing remarkable improvement in computing performance.

在过去的四十年中，计算机的性能大大提高，这使无数的应用程序在我们的日常生活中具有重要的作用。但是，如果没有在提高计算功率效率方面的戏剧性创新，仅持续的半导体设备缩放将无法为将来的计算功能提供足够的性能。对于新兴的挑战，拟议的研究调查了革命性计算范式，以极其有效的方式处理，理解和使用丰富的数据。该方法的新颖性在于通过利用未来工作负载的特征来开发具有软件支持的功率和性能效率计算系统，这些工作量经常使用物理现象的复杂概率数学模型。在这样的应用中，近似计算通常会导致令人满意的结果。同时，它可以通过用简化的电路来替换完整的逻辑功能来大大降低功耗或提高性能，从而模仿功能以进行粗糙计算。为了将近似计算概念扩展到更通用的计算系统，提出了以下整体方法：1）智能微体系结构，以识别编译器支持的不关键的代码的正确性不关键区域； 2）近似计算引擎以有效地执行代码功率和性能区域； 3）高水平的形态基原始人，以处理大量的工作量，并具有更大的功率和性能效率； 4）灵活的体系结构，使程序员和用户可以通过效率来交易计算质量。拟议的研究将对计算机架构，电路和编译器社区产生特定而重大的影响，因为它需要分析有趣和代表性的工作量。实现最先进的电路，架构和编译器基础架构；以及强大而有用的评估方法的发明。由于大多数发展和研究工作将由研究生进行，因此在学生毕业并在其他地方开始就业时，行业和学术界都将受益于受过良好教育和培训的员工以及直接技术转移。最后，这项研究的成功将极大地使我们在科学，技术，商业，医学以及人类努力领域的集体知识中提高人类集体知识的能力，通过允许在计算性能方面显着改善。