  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.
