"Spiking Networks for Improved Cognitive Abilities of Edge Computing De" by Anton Akusok, Yoan Miche et al.
 

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.

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

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