The Relationship between Sparseness and Energy Consumption of Neural Networks
About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of...
Saved in:
| Main Authors: | Guanzheng Wang, Rubin Wang, Wanzeng Kong, Jianhai Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2020/8848901 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Neural Energy Supply-Consumption Properties Based on Hodgkin-Huxley Model
by: Yihong Wang, et al.
Published: (2017-01-01) -
Exploring the Fractal Space of Sparse Deep Neural Network
by: Hang Xu, et al.
Published: (2025-01-01) -
A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting
by: Junchi He, et al.
Published: (2024-12-01) -
Advancing Neural Networks: Innovations and Impacts on Energy Consumption
by: Alina Fedorova, et al.
Published: (2024-12-01) -
ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks
by: Oliver W. Layton, et al.
Published: (2024-11-01)