Name
Abstract | ||
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Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints. In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is. We next formulate the placement problem as a reinforcement learning problem, and show how this problem can be solved with policy gradient optimization. Finally, we describe lessons we have learned from training deep reinforcement learning policies across a variety of placement optimization problems.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3372780.3378174 | ISPD '20: International Symposium on Physical Design
Taipei
Taiwan
September, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7091-2 | 2 |
PageRank | References | Authors |
0.43 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Goldie | 1 | 75 | 5.17 |
| Azalia Mirhoseini | 2 | 238 | 18.68 |