SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning

Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs) are well-known for their high accuracy due to the quality of their automatic feature extraction while brain-inspired h...

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Veröffentlicht in:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design S. 1 - 9
Hauptverfasser:	Nazemi, Mahdi, Esmaili, Amirhossein, Fayyazi, Arash, Pedram, Massoud
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Association on Computer Machinery 02.11.2020
Schlagworte:	adaptability Adaptation models arbitrary NN Artificial neural networks automatic feature extraction brain-inspired hyperdimensional learning models classification components computational efficiency Computational modeling Data models end-to-end hardware realization feature extraction feature extractor HD computing framework HD learning models high-performance HD highly-accurate hyperdimensional learning hybrid machine learning model hybrid model Hyperdimensional Learning learning (artificial intelligence) machine learning models neural nets neural network-based feature extraction neural networks On-line Learning online learning Parallel Processing parameterized hardware implementation synergic machine learning model synergic models Training Training data training time
ISSN:	1558-2434
Online-Zugang:	Volltext
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