Electric Analog Circuit Design with Hypernetworks And A Differential Simulator

Abstract

The manual design of analog circuits is a tedious task of parameter tuning that requires hours of work by human experts. In this work, we make a significant step towards a fully automatic design method that is based on deep learning. The method selects the components and their configuration, as well as their numerical parameters. By contrast, the current literature methods are limited to the parameter fitting part only. A two-stage network is used, which first generates a chain of circuit components and then predicts their parameters. A hypernetwork scheme is used in which a weight generating network, which is conditioned on the circuit's power spectrum, produces the parameters of a primal RNN network that places the components. A differential simulator is used for refining the numerical values of the components. We show that our model provides an efficient design solution, and is superior to alternative solutions.

Machine Learning for Electronic Design Automation: A Survey

“…Li et al [82] focus on extracting well-known building blocks in circuit topology. Recently, Rotman and Wolf [127] use RNN and hypernetwork to generate two-port circuit topology.…”

“…The aforementioned studies do not directly generate a topology. A recent study [127] makes the first attempt to generate circuit topology for given specifications. Their focus is limited to two-port circuits.…”