Modeling throughput and power consumption for real-time concurrent vision application deployments on edge
- 17 August 2026
- journal article
- research article
- Published by Springer Nature in Journal of Real-Time Image Processing
- Vol. 23 (5) , 161
Abstract
No abstract availableKeywords
Funding Information
- HORIZON EUROPE Framework Programme (101092702)
- HORIZON EUROPE Framework Programme (101092702)
- HORIZON EUROPE Framework Programme (101092702)
- HORIZON EUROPE Framework Programme (101092702)
This publication has 37 references indexed in Scilit:
- DipDCE: Offloading-Aware Vision Inference in Edge with Concurrent Executions2025
- Debugging and Profiling Compute Kernels2025
- Edge AI: A Taxonomy, Systematic Review and Future DirectionsCluster Computing, 2024
- Edge AI: A Comprehensive Survey of Technologies, Applications, and Challenges2024
- Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis2022
- AI Multi-Tenancy on Edge: Concurrent Deep Learning Model Executions and Dynamic Model Placements on Edge Devices2021
- Evaluating the power efficiency of deep learning inference on embedded GPU systems2017
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation2016
- Fast Multi-parameter Performance Modeling2016
- Deep Residual Learning for Image Recognition2016