Analog circuit optimization algorithm based on multi-objective acquisition function integrated parallel Bayesian optimization

Abstract

The invention belongs to the field of automatic optimization of analog circuit design parameters in integrated circuit design, in particular to a Gaussian process model-based a circuit optimization method using a Batch Bayesian optimization algorithm. According to the method, in each iteration, a Gaussian process model is firstly constructed, then a plurality of acquisition functions are constructed by the Gaussian process model, multi-objective optimization is carried out on the acquisition functions to obtain Pareto front edges of the acquisition functions, and a plurality of points for circuit simulation are selected from the Pareto front edges. According to the method, the simulation frequency of the circuit in the optimization process can be greatly reduced, analog circuit design parameters meeting the performance requirements are obtained, and meanwhile circuit optimization can be accelerated through the parallel optimization technology.

Description

technical field [0001] The invention belongs to the field of integrated circuit design, and relates to an automatic optimization method for analog circuit design parameters in integrated circuit design, in particular to a circuit optimization method based on a Gaussian process model (Gaussian Process) and using a parallel Bayesian optimization (BatchBayesian Optimization) algorithm. The method can greatly reduce the number of circuit simulations in the optimization process, obtain analog circuit design parameters that meet performance requirements, and can accelerate circuit optimization by using parallel optimization technology. Background technique [0002] Digital integrated circuits have now entered the 10nm process node, and the feature size of analog circuits is also rapidly approaching the 10nm node to reduce power consumption, reduce area, improve performance and integration. With the adoption of new technology, the transistor model in analog circuit design is becomi...

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Problems solved by technology

[0006] Bayesian optimization methods require fewer simulations during optimization, however, Bayesian optimization is a serial optimization algorithm that, during each iteration, optimizes an acquisition function constructed from a Gaussian process model ( Acquisition function) to select points for circuit simulation, so parallel resources cannot be fully utilized

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