Presentation
EVDMARL: Efficient Value Decomposition-based Multi-Agent Reinforcement Learning with Domain-Randomization for Complex Analog Circuit Design Migration
SessionAI for Analog Synthesis
DescriptionAutomated analog circuit design migration significantly alleviates the burden on designers in circuit sizing under various operating conditions. Conventional methods model the migration problem as black-box optimization, requiring excessive iterations of costly simulations to converge. Reinforcement learning exhibits significant promise in transfer learning, as it enables the generation of circuits that fulfill specifications efficiently. The paper proposes a novel value decomposition-based multi-agent reinforcement learning framework, aiming to model complex analog circuits and eliminate the need for manually defined specifications of sub-circuits for new operating conditions. Additionally, it incorporates generalized domain randomization techniques to leverage the varying information across diverse domains. Experiment demonstrates that our algorithm can efficiently generate circuits meeting specifications under new operating conditions in few number of steps, outperforming state-of-the-art methods.
Event Type
Research Manuscript
TimeThursday, June 2710:45am - 11:00am PDT
Location3002, 3rd Floor
EDA
Analog CAD, Simulation, Verification and Test