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AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesis

Jintao Li, Haochang Zhi, Ruiyu Lyu, Wangzhen Li, Zhaori Bi, Keren Zhu, Yanhan Zeng, Weiwei Shan, Changhao Yan, Fan Yang, Yun Li, Xuan Zeng

TL;DR

AnalogGym standardizes the assessment of ML algorithms in analog circuit synthesis and promotes reproducibility with its open datasets and detailed bench-mark specifications, and conducts a comprehensive study of various analog sizing methods on AnalogGym.

Abstract

Recent advances in machine learning (ML) for automating analog circuit synthesis have been significant, yet challenges remain. A critical gap is the lack of a standardized evaluation framework, compounded by various process design kits (PDKs), simulation tools, and a limited variety of circuit topologies. These factors hinder direct comparisons and the validation of algorithms. To address these shortcomings, we introduced AnalogGym, an open-source testing suite designed to provide fair and comprehensive evaluations. AnalogGym includes 30 circuit topologies in five categories: sensing front ends, voltage references, low dropout regulators, amplifiers, and phase-locked loops. It supports several technology nodes for academic and commercial applications and is compatible with commercial simulators such as Cadence Spectre, Synopsys HSPICE, and the open-source simulator Ngspice. AnalogGym standardizes the assessment of ML algorithms in analog circuit synthesis and promotes reproducibility with its open datasets and detailed benchmark specifications. AnalogGym's user-friendly design allows researchers to easily adapt it for robust, transparent comparisons of state-of-the-art methods, while also exposing them to real-world industrial design challenges, enhancing the practical relevance of their work. Additionally, we have conducted a comprehensive comparison study of various analog sizing methods on AnalogGym, highlighting the capabilities and advantages of different approaches. AnalogGym is available in the GitHub repository https://github.com/CODA-Team/AnalogGym. The documentation is also available at http://coda-team.github.io/AnalogGym/.

AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesis

TL;DR

AnalogGym standardizes the assessment of ML algorithms in analog circuit synthesis and promotes reproducibility with its open datasets and detailed bench-mark specifications, and conducts a comprehensive study of various analog sizing methods on AnalogGym.

Abstract

Recent advances in machine learning (ML) for automating analog circuit synthesis have been significant, yet challenges remain. A critical gap is the lack of a standardized evaluation framework, compounded by various process design kits (PDKs), simulation tools, and a limited variety of circuit topologies. These factors hinder direct comparisons and the validation of algorithms. To address these shortcomings, we introduced AnalogGym, an open-source testing suite designed to provide fair and comprehensive evaluations. AnalogGym includes 30 circuit topologies in five categories: sensing front ends, voltage references, low dropout regulators, amplifiers, and phase-locked loops. It supports several technology nodes for academic and commercial applications and is compatible with commercial simulators such as Cadence Spectre, Synopsys HSPICE, and the open-source simulator Ngspice. AnalogGym standardizes the assessment of ML algorithms in analog circuit synthesis and promotes reproducibility with its open datasets and detailed benchmark specifications. AnalogGym's user-friendly design allows researchers to easily adapt it for robust, transparent comparisons of state-of-the-art methods, while also exposing them to real-world industrial design challenges, enhancing the practical relevance of their work. Additionally, we have conducted a comprehensive comparison study of various analog sizing methods on AnalogGym, highlighting the capabilities and advantages of different approaches. AnalogGym is available in the GitHub repository https://github.com/CODA-Team/AnalogGym. The documentation is also available at http://coda-team.github.io/AnalogGym/.
Paper Structure (14 sections, 9 equations, 6 figures, 8 tables)

This paper contains 14 sections, 9 equations, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Testbench Description
  3. The Analog Circuit Synthesis Problem
  4. Problem Definition
  5. PVT Variations
  6. Optimization Objectives and Constraints
  7. Evaluation Metrics of Circuit
  8. Algorithm Evaluation on AnalogGym
  9. Experimental Setup
  10. Single-Objective Optimization
  11. Multi-Objective Optimization
  12. Discussion and Future Directions
  13. Future Directions
  14. Conclusion

Figures (6)

  • Figure 1: Key challenges in analog circuit optimization
  • Figure 2: Comprehensive simulation results for amplifiers: DC, AC, PZ, and transient analyses
  • Figure 3: The designer has to ensure that the performance of ICs is within a certain manageable range.
  • Figure 4: Optimization curves of single-objective optimization algorithms under different topologies.
  • Figure 5: Optimization curves of multi-objective optimization algorithms under different topologies.
  • ...and 1 more figures