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Automated Design of Complex Analog Circuits with Multiagent based Reinforcement Learning

SessionNovel Techniques in Synthesis and Verification of Analog Circuits

DescriptionThe design search space explosion is a key challenge for complex analog circuits design automation. Inspired by multiagent planning theory, we propose three techniques to overcome this issue. Particularly, we (i) partition the circuit into sub-blocks based on circuit topology information and effectively reduce the complexity of design search space; (ii) introduce multiagent-based reinforcement learning and the interactions between agents mimic the design tradeoffs between circuit sub-blocks; (iii) utilize the twin-delayed technique to achieve training stability. Experiments on two analog circuit topologies and two technology nodes demonstrate that multiagent based RL algorithms can achieve better FoM and faster design speed.

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