A Novel Multi-Objective Optimization Framework for Analog Circuit Customization

• Published in: 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE) pp. 1 - 2
• Main Authors: Zhu, Mutian, Hassanpourghadi, Mohsen, Zhang, Qiaochu, Chen, Mike Shuo-Wei, Levi, A. F. J., Gupta, Sandeep
• Format: Conference Proceeding
• Language: English
• Published: EDAA 25.03.2024
• Subjects: Adaptation models; analog circuit; Circuits; Measurement; multi-objective optimization; Sampling methods; Search engines; Simulated annealing; Simulation; surrogate model
• DOI: 10.23919/DATE58400.2024.10546692

Prior research has developed an approach called Analog Mixed-signal Parameter Search Engine (AMPSE) [1] to reduce the cost of design of analog/mixed-signal (AMS) circuits. In this paper, we propose an adaptive sampling method (AS) to identify a range of Pareto-optimal versions of a given AMS circuit with different combinations of metric values to enable parameter-search based methods like AMPSE to efficiently serve multiple users with diverse requirements. As AMS circuit simulation has high run-time complexity, our method uses a surrogate model to estimate the values of metrics for the circuit, given the values of its parameters. In each iteration, we use a mix of uniform and adaptive sampling to identify parameter value combinations, use the surrogate model to identify a subset of these samples to simulate, and use the simulation results to retrain the model. Our method is more effective and has lower complexity compared with prior methods [2]-[4] because it works with any surrogate model, uses a low-complexity yet effective strategy to identify samples for simulation, and uses an adaptive annealing strategy to balance exploration vs. exploitation. Experimental results demonstrate that, at lower complexity, our method discovers better Pareto-optimal designs compared to prior methods. The benefits of our method, relative to prior methods, increase as we move from AMS circuits with low simulation complexities to those with higher simulation complexities. For an AMS circuit with very high simulation complexity, our method identifies designs that are superior to the version of the circuit optimized by experienced designers.