Hardware-constrained edge deep learning
Neural Networks have become commonplace in our daily lives, powering everything from language models in chatbots to computer vision models in industrial machinery. The unending quest for greater model performance has led to an exponential growth in model size. For many devices, especially edge dev...
Saved in:
Main Author: | Ng, Jia Rui |
---|---|
Other Authors: | Weichen Liu |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181190 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hardware constrained deep learning: an empirical analysis of dynamic quantisation across computer vision and natural language processing domains
by: Sai, Shein Htet
Published: (2025) -
Parameterized DNN design for identifying the resource limitations of edge deep learning hardware
by: Aung, Shin Thant
Published: (2024) -
Detecting ransomware using deep learning and hardware performance counters
by: Hashil Jugjivan
Published: (2025) -
Audio intelligence & domain adaptation for deep learning models at the edge
by: Ng, Linus JunJia
Published: (2021) -
Energy efficient scheduling for deadline-constrained applications in edge computing systems
by: Wang, Qianteng
Published: (2024)