What question did this study set out to answer?

The aim is to showcase how open-source AI can effectively address analog and RF non-idealities.

May 29, 20260 citationsOpen Access

Open-Source AI for Analog Correction: From RF Power Amplifiers to Energy-Efficient Silicon

Key Points

The aim is to showcase how open-source AI can effectively address analog and RF non-idealities.
Introduced OpenDPD, a framework for linearizing RF power amplifiers.
Optimized mixed-precision and sparse AI models for circuit design.
Discussed algorithm deployment in various circuit components like ADCs and PLLs.
Demonstrated effective co-design processes for AI models and hardware accelerators.
Outlined significant improvements in the efficiency of analog/RF designs.
Highlighted successful deployment strategies aiding broader adoption in the industry.

Abstract

Slides for the talk F5.4, "Open-Source AI for Analog Correction: From RF Power Amplifiers to Energy-Efficient Silicon", presented by Chang Gao at ISSCC 2026 Forum 5, "Analog for AI and AI for Analog: What the Analog/RF People Can Do and Leverage in the AI Era", on February 19, 2026.AI offers a new paradigm for correcting analog/RF non-idealities, but challenges in design, benchmarking, and deployment hinder its adoption. This talk argues for an open-source approach to bridge these gaps. It introduces OpenDPD, a framework for PA linearization that has enabled co-design of optimized mixed-precision and sparse AI models and AI-DPD hardware accelerators, and discusses the path from algorithm to silicon with extensions to circuits such as ADCs and PLLs.

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