Smart sensors using artificial intelligence for on-detector electronics and ASICs

TL;DR

This paper explores the integration of artificial intelligence into on-detector electronics and ASICs to enhance data processing, reduce system complexity, and enable faster, lower-power feedback in particle physics experiments.

Contribution

It discusses motivations, potential applications, and unique requirements for on-detector AI, and outlines opportunities for advancing machine learning, co-design workflows, and microelectronics technologies.

Findings

Review of current AI hardware for particle physics

Identification of design challenges and opportunities

Proposals for future AI-enabled detector systems

Abstract

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.