A Resource-efficient Task Scheduling System using Reinforcement Learning

Computer-aided design (CAD) tools typically incorporate thousands or millions of functional tasks and dependencies to implement various synthesis and analysis algorithms. Efficiently scheduling these tasks in a computing environment that comprises manycore CPUs and GPUs is critically important because it governs the macro-scale performance. However, existing scheduling methods are typically hardcoded within an application that are not adaptive to the change of computing environment. To overcome this challenge, this paper will introduce a novel reinforcement learning-based scheduling algorithm that can learn to adapt the performance optimization to a given runtime (task execution environment) situation. We will present a case study on VLSI timing analysis to demonstrate the effectiveness of our learning-based scheduling algorithm. For instance, our algorithm can achieve the same performance of the baseline while using only 20% of CPU resources.