A Neural Network Approach to Analog Circuit Design Optimization using Nesterov's Accelerated Quasi-Newton Method

摘要

With the rising need for high-performance circuits, electronic design automation has gained significant attention. Analog circuit design optimization is complex mainly due to the large search space and high non-linearity. Neural networks have shown to be effective in solving highly non-linear problems. Training algorithms play an important role in neural networks. Gradient-based algorithms are popularly used in the training of neural networks. They can be broadly classified into first and second-order methods. While first-order methods are simple and low in computational complexity, they are slow in convergence when applied to highly non-linear problems. Incorporating second order curvature information has shown to significantly improve convergence despite its high computational cost. This paper proposes a second order adaptive modified Nesterov's accelerated quasi-Newton method (amNAQ) with an adaptive momentum scheme. The performance of the proposed method is evaluated in transistor sizing of a two stage op-amp. The results indicate that the proposed method can efficiently deduce the size while satisfying the desired specifications much faster compared to the existing popular algorithms.