Deep Reinforcement Learning for Optimization at Early Design Stages
Sprache des Titels:
Englisch
Original Kurzfassung:
In this paper, we introduce Deep Reinforcement
Learning (DRL) for design cost optimization at early stages of
the System on Chips (SoCs) design process. We demonstrate
that DRL is a suitable solution for the problem at hand. We
benchmark three DRL algorithms based on Pointer Network, a
neural network specifically applied for combinatorial problems,
on the design cost optimization. We show that this lead to the
considerable improvements in cost optimization compared to
conventional optimization methods. Additionally, by using the
recently introduced RUDDER method and its reward redistribution approach, we obtain a significant improvement in complex
designs.
Forschungs-