Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning

Lorenzo Servadei, Jiapeng Zheng, José Arjona-Medina, Michael Werner, Volkan Esen, Sepp Hochreiter, Wolfgang Ecker, Robert Wille

Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 2020

With the increase in the complexity of the modern system on Chips(SoCs) and the demand for a lower time-to-market, automation becomes essential in hardware design. This is particularly relevant in complex/time-consuming tasks, as the optimization of design cost for a hardware component. Design cost, in fact, may depend on several objectives, as for the hardware-software trade-off. Given the complexity of this task, the designer often has no means to perform a fast and effective optimization in particular for larger and complex designs. In this paper, we introduce Deep Reinforcement Learning(DRL) for design cost optimization at the early stages of the design process. We first show that DRL is a perfectly suitable solution for the problem at hand. Afterward, by means of a Pointer Network, a neural network specifically applied for combinatorial problems, we benchmark three DRL algorithms towards the selected problem. Results obtained in different settings show the improvements achieved by DRL algorithms compared to conventional optimization methods. Additionally, by using reward redistribution proposed in the recently introduced RUDDER method, we obtain significant improvements in complex designs. Here, the obtained optimization is on average 15.18% on the area as well as 8.25% and 8.12% on the application size and execution time on a dataset of industrial hardware/software interface design