M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling
This work addresses computational overhead and customization challenges in analog circuit optimization for circuit designers, though it appears incremental as it builds on existing RL and Mamba techniques.
The paper tackles the problem of analog circuit optimization by proposing M3, a model-based reinforcement learning method that uses the Mamba architecture and effective scheduling to improve sample efficiency across multiple circuits with different parameters and specifications, achieving significant gains compared to existing RL methods.
Recent advancements in reinforcement learning (RL) for analog circuit optimization have demonstrated significant potential for improving sample efficiency and generalization across diverse circuit topologies and target specifications. However, there are challenges such as high computational overhead, the need for bespoke models for each circuit. To address them, we propose M3, a novel Model-based RL (MBRL) method employing the Mamba architecture and effective scheduling. The Mamba architecture, known as a strong alternative to the transformer architecture, enables multi-circuit optimization with distinct parameters and target specifications. The effective scheduling strategy enhances sample efficiency by adjusting crucial MBRL training parameters. To the best of our knowledge, M3 is the first method for multi-circuit optimization by leveraging both the Mamba architecture and a MBRL with effective scheduling. As a result, it significantly improves sample efficiency compared to existing RL methods.