Journal
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
Volume 39, Issue 10, Pages 2640-2653Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2019.2961322
Keywords
Optimization; Data models; Computational modeling; Integrated circuit modeling; SPICE; Artificial neural networks; Analog circuits; Analog circuit optimization; artificial neural network (ANN); genetic algorithm (GA); global optimization; local optimization
Categories
Funding
- Shandong Provincial Key Research and Development Plan [2018GGX101010]
- Shandong Provincial Key Technology Innovation Project [2019JZZY010203]
Ask authors/readers for more resources
This article presents a new analog circuit optimization system for automated sizing of analog integrated circuits. It consists of a genetic algorithm (GA)-based global optimization engine and an artificial neural network (ANN)-based local optimization engine. The key new idea is to use parallel computation to train ANN models for design space neighborhoods thus the local minimum search (LMS) can have a much faster search speed. For the GA-based global optimization, circuit performances are calculated by parallel SPICE simulations. For the LMS, circuit performance data are derived from ANN model predictions instead of SPICE simulations. Since the most time for an ANN-based LMS is spent on SPICE calls which can be run in parallel, the LMS process can also exploit the multiple core configuration of a modern computational server in addition to the GA global search. The fully parallelized optimization system is deployed to design a two-stage rail-to-rail operational amplifier and a fifth-order active-RC Chebyshev complex band-pass filter. The experimental results show that the proposed method provides about four times speed enhancement and comparable results compared with traditional approaches employing the same parallel global optimization but sequential SPICE calls during LMS.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available