9 citations · 42 across the 19 of their papers we have counts for
5 papers · 1 filter
Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge
Amir Taherin, José Cano, Bin Ren +2
Video object detection on edge devices runs computationally expensive detectors over long frame streams, causing high energy consumption and sustained GPU utilization. Although con…
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Fai…
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition
Nikolaos Louloudakis, Perry Gibson, José Cano +1
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is erro…
Optimizing Data Processing in Space for Object Detection in Satellite Imagery
Martina Lofqvist, José Cano
There is a proliferation in the number of satellites launched each year, resulting in downlinking of terabytes of data each day. The data received by ground stations is often unpro…
Accelerating Deep Learning Applications in Space
Martina Lofqvist, José Cano
Computing at the edge offers intriguing possibilities for the development of autonomy and artificial intelligence. The advancements in autonomous technologies and the resurgence of…