CSR---SMA: Computer Architecture Optimization: A Machine Learning Approach

CSR---SMA：计算机架构优化：一种机器学习方法

• 批准号: 0720773
• 负责人: Jose Martinez
• 金额: $ 50万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720773&HistoricalAwards=false
• 关键词: CSR; SMA; Computer; Architecture; Optimization

Exponential increases in transistor densities at each new technology generation have allowed us to build chips with greatly enhanced capabilities and functionality. The last twenty years have witnessed the introduction and adoption of numerous architectural advances at the processor level, as computer architects have successfully translated increases in transistor budgets to performance. Unfortunately, effective hardware policies for managing and controlling these complex artifacts have not advanced commensurately. Most policies are ad hoc at best, and generally incapable of providing important functionalities like anticipating the long-term consequences of decisions (planning), or generalizing from experience obtained through decisions executed in the past to act successfully in new situations (learning).At the same time, the artificial intelligence and machine learning communities have made tremendous strides in designing computer programs and algorithms that learn about their environment and improve automatically with experience. The proposed inter-disciplinary work will develop methodologies based on such a technology to design efficient, adaptable, and self-optimizing on-chip hardware policies. The project will concentrate on chip multiprocessors, in which opportunities for hardware management promise to be numerous and challenging. If successful, this approach may set off a change in the way computer architects think about and conduct research on computer architecture design.The project will apply machine learning technology in two ways: (1) tools for the systematic design of optimized management policies that can then be installed in hardware (e.g., ROM-based circuits); and (2) self-optimizing hardware agents that implement efficient policies, can learn from their environment, and improve automatically with experience.

每一代新技术的晶体管密度都呈指数级增长，这使得我们能够构建具有极大增强的能力和功能的芯片。在过去的二十年里，随着计算机架构师成功地将晶体管预算的增加转化为性能，在处理器层面上引入和采用了许多架构上的进步。不幸的是，用于管理和控制这些复杂工件的有效硬件策略并没有随之发展。大多数政策充其量是临时性的，通常无法提供重要的功能，如预测决策的长期后果（计划），或从过去执行的决策中获得的经验中总结，以便在新的情况下成功地采取行动（学习）。与此同时，人工智能和机器学习社区在设计能够学习的计算机程序和算法方面取得了巨大的进步，并随着经验自动改进。拟议的跨学科工作将开发基于这种技术的方法，以设计高效、适应性强和自优化的片上硬件策略。该项目将集中在芯片多处理器，其中硬件管理的机会承诺是众多的和具有挑战性的。如果成功的话，这种方法可能会引发计算机架构师思考和研究计算机架构设计的方式发生变化。该项目将以两种方式应用机器学习技术：（1）用于系统设计优化管理策略的工具，然后可以安装在硬件中（例如，基于ROM的电路）;以及（2）实现有效策略的自优化硬件代理，可以从它们的环境中学习，并根据经验自动改进。