| Potential of edge machine learning for instrumentation AC Therrien, B Gouin-Ferland, MM Rahimifar Applied optics 61 (8), 1930-1937, 2022 | 8 | 2022 |
| Combining optimized quantization and machine learning for real-time data reduction at the edge B Gouin-Ferland, MM Rahimifar, CÉ Granger, Q Wingering, R Coffee, ... 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC …, 2022 | 3 | 2022 |
| Data reduction through optimized scalar quantization for more compact neural networks B Gouin-Ferland, R Coffee, AC Therrien Frontiers in Physics 10, 957128, 2022 | 3 | 2022 |
| A survey of machine learning to fpga tool-flows for instrumentation MM Rahimifar, CÉ Granger, Q Wingering, B Gouin-Ferland, HE Rahali, ... 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC …, 2022 | 2 | 2022 |
| Exploring machine learning to hardware implementations for large data rate x-ray instrumentation MM Rahimifar, Q Wingering, B Gouin-Ferland, HE Rahali, CÉ Granger, ... Machine Learning: Science and Technology 4 (4), 045035, 2023 | 1 | 2023 |
| Towards Efficient Data Processing on the Edge With Neuromorphic Computing for Instrumentation B Gouin-Ferland, Q Wingering, R Coffee, AC Therrien 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and …, 2023 | | 2023 |
| Building Real Time Edge Machine Learning Systems for High Data Rate Acquisition MM Rahimifar, Q Wingering, B Gouin-Ferland, R Coffee, AC Therrien 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and …, 2023 | | 2023 |
| Ongoing challenges with Edge Machine Learning for Radiation Instrumentation AC Therrien, X Groleau, B Gouin-Ferland Digital Holography and Three-Dimensional Imaging, DTh4F. 3, 2021 | | 2021 |
Audrey Corbeil TherrienUniversité de SherbrookeVerified email at usherbrooke.ca
