The Relationship between Sparseness and Energy Consumption of Neural Networks
Joint Authors
Zhang, Jianhai
Wang, Guanzheng
Wang, Rubin
Kong, Wanzeng
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-25
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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 a neural network, that is, the sparseness, affects the energy consumption of a neural network.
Laughlin’s studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs.
Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks.
In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments.
The ratio of signaling costs to fixed costs is between 1.3 and 2.1.
We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks.
The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4.
Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions.
The calculation results of this paper may be helpful to the study of neural coding.
American Psychological Association (APA)
Wang, Guanzheng& Wang, Rubin& Kong, Wanzeng& Zhang, Jianhai. 2020. The Relationship between Sparseness and Energy Consumption of Neural Networks. Neural Plasticity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1202922
Modern Language Association (MLA)
Wang, Guanzheng…[et al.]. The Relationship between Sparseness and Energy Consumption of Neural Networks. Neural Plasticity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1202922
American Medical Association (AMA)
Wang, Guanzheng& Wang, Rubin& Kong, Wanzeng& Zhang, Jianhai. The Relationship between Sparseness and Energy Consumption of Neural Networks. Neural Plasticity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1202922
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202922