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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| Vydáno v: | Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9 |
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| Jazyk: | angličtina |
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Association on Computer Machinery
02.11.2020
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| ISSN: | 1558-2434 |
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| Abstract | 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 hyperdimensional (HD) learning models are famous for their quick training, computational efficiency, and adaptability. This work presents a hybrid, synergic machine learning model that excels at all the said characteristics and is suitable for incremental, on-line learning on a chip. The proposed model comprises an NN and a classifier. The NN acts as a feature extractor and is specifically trained to work well with the classifier that employs the HD computing framework. This work also presents a parameterized hardware implementation of the said feature extraction and classification components while introducing a compiler that maps any arbitrary NN and/or classifier to the aforementioned hardware. The proposed hybrid machine learning model has the same level of accuracy (i.e. ±1%) as NNs while achieving at least 10% improvement in accuracy compared to HD learning models. Additionally, the end-to-end hardware realization of the hybrid model improves power efficiency by 1.60x compared to state-of-the-art, high-performance HD learning implementations while improving latency by 2.13x. These results have profound implications for the application of such synergic models in challenging cognitive tasks. |
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| AbstractList | 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 hyperdimensional (HD) learning models are famous for their quick training, computational efficiency, and adaptability. This work presents a hybrid, synergic machine learning model that excels at all the said characteristics and is suitable for incremental, on-line learning on a chip. The proposed model comprises an NN and a classifier. The NN acts as a feature extractor and is specifically trained to work well with the classifier that employs the HD computing framework. This work also presents a parameterized hardware implementation of the said feature extraction and classification components while introducing a compiler that maps any arbitrary NN and/or classifier to the aforementioned hardware. The proposed hybrid machine learning model has the same level of accuracy (i.e. ±1%) as NNs while achieving at least 10% improvement in accuracy compared to HD learning models. Additionally, the end-to-end hardware realization of the hybrid model improves power efficiency by 1.60x compared to state-of-the-art, high-performance HD learning implementations while improving latency by 2.13x. These results have profound implications for the application of such synergic models in challenging cognitive tasks. |
| Author | Fayyazi, Arash Nazemi, Mahdi Esmaili, Amirhossein Pedram, Massoud |
| Author_xml | – sequence: 1 givenname: Mahdi surname: Nazemi fullname: Nazemi, Mahdi email: mnazemi@usc.edu organization: University of Southern California – sequence: 2 givenname: Amirhossein surname: Esmaili fullname: Esmaili, Amirhossein email: esmailid@usc.edu organization: University of Southern California – sequence: 3 givenname: Arash surname: Fayyazi fullname: Fayyazi, Arash email: fayyazi@usc.edu organization: University of Southern California – sequence: 4 givenname: Massoud surname: Pedram fullname: Pedram, Massoud email: pedram@usc.edu organization: University of Southern California |
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| Snippet | Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example,... |
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| SubjectTerms | 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 |
| Title | SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning |
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