Smart sensors using artificial intelligence for on-detector electronics and ASICs
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.
Keywords
Cite
@article{arxiv.2204.13223,
title = {Smart sensors using artificial intelligence for on-detector electronics and ASICs},
author = {Gabriella Carini and Grzegorz Deptuch and Jennet Dickinson and Dionisio Doering and Angelo Dragone and Farah Fahim and Philip Harris and Ryan Herbst and Christian Herwig and Jin Huang and Soumyajit Mandal and Cristina Mantilla Suarez and Allison McCarn Deiana and Sandeep Miryala and F. Mitchell Newcomer and Benjamin Parpillon and Veljko Radeka and Dylan Rankin and Yihui Ren and Lorenzo Rota and Larry Ruckman and Nhan Tran},
journal= {arXiv preprint arXiv:2204.13223},
year = {2022}
}
Comments
Contribution to Snowmass 2021; 27 pages, 6 figures