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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Bibliographic Details
Published in:2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 775 - 780
Main Authors: Kim, Yeseong, Kim, Jiseung, Imani, Mohsen
Format: Conference Proceeding
Language:English
Published: IEEE 05.12.2021
Subjects:
Buildings
Computational modeling
Deep learning
Design automation
Edge Computing
Energy efficiency
Hyperdimensional Computing
Many-class classification
Performance evaluation
Training
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Summary: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.
DOI:10.1109/DAC18074.2021.9586235