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: 0953603
• 负责人: Nam Sung Kim
• 金额: $ 43.7万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-15 至 2015-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953603&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.

在过去的四十年里，计算机的性能有了巨大的提高，这使得无数的应用程序在我们的日常生活中发挥了重要作用。然而，如果没有在提高计算的功率效率方面的显著创新，仅持续的半导体器件缩放将无法为未来的计算能力提供足够的性能。对于新出现的挑战，拟议的研究调查了革命性的计算范式，以极其有效的方式处理，理解和使用大量数据。该方法的新奇在于开发具有软件支持的功率和性能高效的计算系统，通过利用未来工作负载的特性，这些工作负载通常使用物理现象的复杂概率数学模型。在这样的应用中，近似计算通常可以得到令人满意的结果。同时，它可以通过用模拟粗略计算功能的简化电路代替完整的逻辑功能来显著降低功耗或提高性能。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，以识别具有编译器支持的代码的正确性非关键区域; 2）近似计算引擎，以有效地执行这些代码功率和性能区域; 3）高级形态原语，以更大数量级的功率和性能效率处理大部分工作负载; 4）灵活的体系结构，允许程序员和用户在计算质量和效率之间进行权衡。拟议的研究将对计算机体系结构，电路和编译器社区产生具体而重大的影响，因为它需要分析有趣的和有代表性的工作负载;实现最先进的电路，体系结构和编译器基础设施;以及发明强大而有用的评估方法。由于大部分开发和研究工作将由研究生进行，工业和学术界都将受益于受过良好教育和培训的员工，以及学生毕业后开始在其他地方就业时的直接技术转让。最后，这项研究的成功将极大地有利于我们的能力，以促进人类？在科学、技术、商业、医学和几乎所有其他人类奋进领域的集体知识，通过允许计算性能的显着改进。