GA-SVM Optimization Kernel applied to Analog IC Design Automation

This paper presents a circuit/system level synthesis and optimization approach based on a learning scheme using support vectors machines (SVMs) and evolutionary strategies applied to the design of analog and mixed-signal ICs. This approach combines the best qualities of these two techniques, a robust classification and regression method and a powerful global optimization. The SVM is used to dynamically model performance space and identify the feasible design space regions while at the same time the evolutionary techniques are looking for the global optimum. Finally, the proposed optimization-based approach is demonstrated for the design of some analog circuits using HSPICE as the evaluation engine.