Collaborative Distillation Meta Learning for Simulation Intensive Hardware Design
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by
Haeyeon Kim, Minsu Kim, Joungho Kim, Jinkyoo Park
2022
Abstract
This paper proposes a novel collaborative distillation meta learning (CDML)
framework for simulation intensive hardware design problems. Deep reinforcement
learning (DRL) has shown promising performance in various hardware design
problems. However, previous works on DRL-based hardware design only dealt with
problems with simplified objectives, which are not practical. In fact, the
objective evaluation of real-world electrical performance through simulation is
costly in terms of both time and computation, making DRL scheme involving
extensive reward calculations not suitable. In this paper, we apply the CDML
framework to decoupling capacitor placement problem (DPP), one of the
significant simulation intensive hardware design problems. The CDML framework
consists of a context-based meta learner and collaborative distillation scheme
to produce a reusable solver. The context-based meta learner captures the
location of probing port (i.e., target circuit block) and improves
generalization capability. The collaborative distillation scheme with
equivariant label transformation imposes the action-permutation
(AP)-equivariant nature of placement problems, which not only improves sample
efficiency but also improves generalization capability. Extensive experimental
results verified that our CDML outperforms both neural baselines and iterative
conventional design methods in terms of real-world objective, power integrity,
with zero-shot transfer-ability.
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