Abstract
This Concept Paper Highlights a Recently Opened Opportunity for Large Scale Analytical Algorithms to Be Trained Directly on Edge Devices. Such Approach is a Response to the Arising Need of Processing Data Generated by Natural Person (A Human Being), Also Known as Personal Data. Spiking Neural Networks Are the Core Method Behind It: Suitable for a Low Latency Energy-Constrained Hardware, Enabling Local Training or Re-Training, While Not Taking Advantage of Scalability Available in the Cloud.
Recommended Citation
A. Akusok et al., "Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices," ACM International Conference Proceeding Series, pp. 307 - 308, Association for Computing Machinery, Jun 2019.
The definitive version is available at https://doi.org/10.1145/3316782.3321546
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Edge computing; Interactive computation; Spiking neural networks
International Standard Book Number (ISBN)
978-145036232-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
05 Jun 2019
