Systems

Multi-Agent Reinforcement Learning for Hardware Architecture Search: A Case Study on Domain-Specific DRAM Memory Controller Design
Reinforement Learning can potentially be a powerful tool for solving complex combinatorial optimization problems, such as microprocessor desgin. Here, we show that a multi-agent RL approach outperforms past work using single agent RL, since the problem can easily be decomposed into designing independent sub-systems.
S. Krishnan, Natasha Jaques, S. Omidshafiei, D. Zhang, I. Gur, V. J. Reddi, S. Faust
2022 In Preprint
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Multi-Agent Reinforcement Learning for Hardware Architecture Search: A Case Study on Domain-Specific DRAM Memory Controller Design