indylab / Circuit-Synthesis

An approach for Circuit Synthesis using Dataset Threshold queries.

Learning to Design Analog Circuits to Meet Threshold Specifications

Overview

Code for Learning to Design Analog Circuits to Meet Threshold Specifications (Accepted by ICML 2023 as Poster)

• Preprint Coming Soon
• Website Comning Soon

Docker

To export running results to host machine:

1. Create two folders, one for out_plot and another for result_out.
2. Run the docker using command

docker run -v {absolute path to out_plot folder}:/Circuit-Synthesis/out_plot -v {absolute path to result_out folder}:/Circuit-Synthesis/result_out {docker image name} --path={Train config path}

Usage

Problem 1

Base dataset, DL, across data sizes with base test dataset, success rate as function of (two-sided) error margin:

python main.py --path=./config/config_template/problem1-compare-datasize-relative-Error-margin.yaml

Problem 2 Number 1 and Number 4

Compare datasets construction methods using deep learning, 10-fold Cross Validation as a function of error margin:

python main.py --path=./config/config_template/problem2-compare-dataset-DL-10fold-absolute-Error-margin.yaml

Problem 2 Number 2

Test success rate; Compare training methods (DL, lookup, RF, …) with “softargmax”, 10-fold cross validation as a function of error margin:

python main.py --path=./config/config_template/problem2-compare-method-Softargmax-10fold-absolute-Error-margin.yaml

Problem 2 Number 3

Test success rate; Compare data sizes with DL, “softargmax” as a function of error margin:

python main.py --path=./config/config_template/problem2-compare-datasize-softArgmax-DL-Absolute-Error-margin.yaml

Citation

(Coming soon)