Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
Workshop on Machine Learning for CAD (MLCAD)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
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. Afterwards, 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.
Forschungs-