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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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一种基于强化学习的方法,用于在多FPGA平台上的高效路由.

Umer Farooq1, Habib Mehrez2, Najam Ul Hasan3

  • 1School of Computer Science and Engineering, University of Sunderland, Sunderland SR6 0DD, UK.

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概括

本研究引入了强化学习 (RL) 框架,以加速多FPGA原型设计中的FPGA间路由. RL方法显著减少了路由时间,从而导致更快的整体设计后端流.

关键词:
后端流动流程的后端流程在FPGA之间进行路由.在多FPGA平台上.原型设计原型设计.强化学习是一种强化学习.

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科学领域:

  • 计算机工程 计算机工程
  • 硬件加速器 硬件加速器
  • 人工智能的人工智能

背景情况:

  • 多FPGA原型设计可实现现实世界的测试和循环精确的设计验证.
  • 跨FPGA路由是多FPGA原型设计中的一个关键和耗时的步骤.
  • 跨FPGA路由的复杂性随着现代硬件设计而增加.

研究的目的:

  • 开发和评估基于强化学习 (RL) 的框架,以加快FPGA之间的路由过程.
  • 在不影响结果质量 (QoR) 的情况下,优化RL框架的勘探-开发权衡 (ε-贪的方法).

主要方法:

  • 整合基于RL的框架,用于FPGA之间的路由.
  • 在RL框架内实施一个e-greedy战略.
  • 通过使用14个复杂的基准,对已建立的可路由性驱动和时间驱动的路由方法进行比较分析.

主要成果:

  • 与路由能力驱动的方法相比,拟议的RL框架在FPGA间路由中实现了平均45%的加快速度.
  • 与时间驱动的路由方法相比,RL框架平均提供了32%的加快速度.
  • 总体后端流速加快的22%和15%被观察到与路由性和时间驱动的方法,分别.

结论:

  • 基于RL的框架有效地加速了多FPGA原型设计中的FPGA间路由.
  • 这种加速有助于显著提高硬件设计后端流程的整体速度.
  • 拟议的方法为复杂的原型设计挑战提供了一个有希望的解决方案.