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

在每一代新技术中，晶体管密度呈指数级增长，使我们能够制造具有极大增强能力和功能的芯片。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，在处理器级别引入和采用了大量的体系结构进步。不幸的是，用于管理和控制这些复杂构件的有效硬件策略并没有相应地发展。大多数策略充其量是临时的，通常不能提供重要的功能，如预测决策的长期后果(规划)，或从过去执行的决策中获得的经验中总结出来，以在新的情况下成功行动(学习)。与此同时，人工智能和机器学习社区在设计计算机程序和算法方面取得了巨大的进步，这些程序和算法可以学习他们的环境并根据经验自动改进。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、适应性和自我优化的片上硬件策略。该项目将专注于芯片多处理器，其中硬件管理的机会有望是众多的和具有挑战性的。如果成功，这种方法可能会改变计算机架构师思考和进行计算机体系结构设计研究的方式。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(例如，基于只读存储器的电路)中；(2)实现有效策略的自我优化硬件代理，可以从他们的环境中学习，并通过经验自动改进。

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.