CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing
The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less powerful systems such as edge computing devices. However, the...
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| Vydáno v: | 2021 58th ACM/IEEE Design Automation Conference (DAC) s. 775 - 780 |
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05.12.2021
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| Abstract | The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less powerful systems such as edge computing devices. However, the many-class classification problem is beyond the focus of mainstream HDC research; the existing HDC would not provide sufficient quality and efficiency due to its coarse-grained training. In this paper, we propose an efficient many-class learning framework, called CascadeHD, which identifies latent high-dimensional patterns of many classes holistically while learning a hierarchical inference structure using a novel meta-learning algorithm for high efficiency. Our evaluation conducted on the NVIDIA Jetson device family shows that CascadeHD improves the accuracy for many-class classification by up to 18% while achieving 32% speedup compared to the existing HDC. |
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| AbstractList | The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less powerful systems such as edge computing devices. However, the many-class classification problem is beyond the focus of mainstream HDC research; the existing HDC would not provide sufficient quality and efficiency due to its coarse-grained training. In this paper, we propose an efficient many-class learning framework, called CascadeHD, which identifies latent high-dimensional patterns of many classes holistically while learning a hierarchical inference structure using a novel meta-learning algorithm for high efficiency. Our evaluation conducted on the NVIDIA Jetson device family shows that CascadeHD improves the accuracy for many-class classification by up to 18% while achieving 32% speedup compared to the existing HDC. |
| Author | Kim, Yeseong Kim, Jiseung Imani, Mohsen |
| Author_xml | – sequence: 1 givenname: Yeseong surname: Kim fullname: Kim, Yeseong email: yeseongkim@dgist.ac.kr organization: DGIST – sequence: 2 givenname: Jiseung surname: Kim fullname: Kim, Jiseung email: js980408@dgist.ac.kr organization: DGIST – sequence: 3 givenname: Mohsen surname: Imani fullname: Imani, Mohsen email: m.imani@uci.edu organization: UC Irvine |
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| Snippet | The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural... |
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| SubjectTerms | Buildings Computational modeling Deep learning Design automation Edge Computing Energy efficiency Hyperdimensional Computing Many-class classification Performance evaluation Training |
| Title | CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing |
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