3 papers
cs.DC2026
Where to Split? A Pareto-Front Analysis of DNN Partitioning for Edge Inference
Adiba Masud, Nicholas Foley, Pragathi Durga Rajarajan +1
The deployment of deep neural networks (DNNs) on resource-constrained edge devices is frequently hindered by their significant computational and memory requirements. While partitio…
cs.CV2024
EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder
Hasanul Mahmud, Kevin Desai, Palden Lama +1
Image classification is a fundamental task in computer vision, and the quest to enhance DNN accuracy without inflating model size or latency remains a pressing concern. We make a c…
cs.LG2024
A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge
Hasanul Mahmud, Peng Kang, Kevin Desai +2
Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resource-constrained edg…