Smart-MSP: A Self-Adaptive Multiple Starting Point Optimization Approach for Analog Circuit Synthesis

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Automated analog circuit design is promising for increasing the design productivity and narrowing the time-to-market, but is facing the bottleneck of tremendous design complexity. In this paper, a simulation-based optimization approach named smart-multiple starting point (MSP) is proposed for analog circuit synthesis. The proposed smart-MSP is based on the framework of MSP optimization, which is shown to be much more efficient than other global optimization methods like simulated annealing, genetic algorithm, particle swarm optimization, etc. Efficient techniques including heuristic-biased starting point selection, sparse regression and probabilistic TABU are developed in smart-MSP and make the algorithm quite smart in a way that the overall optimization process is self-adaptive by learning from the previous local searches and can efficiently produce optimal results to approximate the global optimum. Experiments have demonstrated that the proposed smart-MSP is ${2.6-12.5\times }$ faster than the original MSP method, and is $ {1.3-2100\times }$ faster than other state-of-the-art methods.