EdgeNAS: discovering efficient neural architectures for edge systems
Edge systems integrated with deep neural networks (DNNs) are deemed to pave the way for future artificial intelligence (AI). However, designing accurate and efficient DNNs for resource-limited edge systems is challenging as well as requires a huge amount of engineering efforts from human experts sin...
Main Authors: | Luo, Xiangzhong, Liu, Di, Kong, Hao, Liu, Weichen |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Conference Paper |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/165560 |
Similar Items
-
SurgeNAS: a comprehensive surgery on hardware-aware differentiable neural architecture search
by: Luo, Xiangzhong, et al.
Published: (2023) -
Designing efficient DNNs via hardware-aware neural architecture search and beyond
by: Luo, Xiangzhong, et al.
Published: (2022) -
Collate: collaborative neural network learning for latency-critical edge systems
by: Huai, Shuo, et al.
Published: (2023) -
Work-in-progress: what to expect of early training statistics? An investigation on hardware-aware neural architecture search
by: Luo, Xiangzhong, et al.
Published: (2023) -
EMNAPE: efficient multi-dimensional neural architecture pruning for EdgeAI
by: Kong, Hao, et al.
Published: (2023)