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.
Sensors (Basel, Switzerland)
|January 11, 2025
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.
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.
相关概念视频
01:22Multi-input and Multi-variable systems
96
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
96
01:24Reinforcement Schedules
130
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.
Once a behavior is learned,...
Once a behavior is learned,...
130