Abstract
New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within the detector hardware will reduce the data volumes generated, thereby reducing material cost, power, and data management requirements. Several solutions are already being developed both on the hardware and on the software side to facilitate the use of machine learning as a real-time data analysis solution.
Subject
Atomic and Molecular Physics, and Optics,Engineering (miscellaneous),Electrical and Electronic Engineering
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Efficient compression at the edge for real-time data acquisition in a billion-pixel X-ray camera;Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment;2024-01
2. Exploring machine learning to hardware implementations for large data rate x-ray instrumentation;Machine Learning: Science and Technology;2023-11-24
3. A Versatile Edge Machine Learning Test Bench for High Bandwidth Instrumentation;2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD);2023-11-04
4. Building Real Time Edge Machine Learning Systems for High Data Rate Acquisition;2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD);2023-11-04
5. Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning;Machine Learning: Science and Technology;2023-10-30