Compressing fully connected layers of deep neural networks using permuted features
Abstract Modern deep neural networks typically have some fully connected layers at the final classification stages. These stages have large memory requirements that can be expensive on resource‐constrained embedded devices and also consume significant energy just to read the parameters from external...
Saved in:
Main Authors: | Dara Nagaraju, Nitin Chandrachoodan |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-07-01
|
Series: | IET Computers & Digital Techniques |
Subjects: | |
Online Access: | https://doi.org/10.1049/cdt2.12060 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sensor fusion with high‐order moments constraints using projection‐based neural network
by: Yousef Alipouri, et al.
Published: (2021-10-01) -
Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting
by: Chun‐Yao Lee, et al.
Published: (2024-10-01) -
Integration of Artificial Neural Network and Taguchi Method for Prediction and Minimisation of Thick-Walled Polypropylene Gear Shrinkage
by: Bikram Singh Solanki, et al.
Published: (2025-08-01) -
Maximising Strawberry Yield in Single-Layered and Double-Layered Gothic Greenhouses: A Microclimate Approach
by: T. D. Akpenpuun, et al.
Published: (2023-07-01) -
Maximising Strawberry Yield in Single-Layered and Double-Layered Gothic Greenhouses: A Microclimate Approach
by: T. D. Akpenpuun, et al.
Published: (2023-07-01)