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CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing
Time
Location
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
AI/ML System Design
Topics
Design
DescriptionThe brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight learning solution alternative to deep neural networks. Prior research shows the effectiveness of HDC-based learning on less-powerful systems. However, the many-class classification problem is beyond the focus of mainstream HDC research. 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 shows that CascadeHD improves the accuracy for many-class classification by up to 18\% while achieving 32\% speedup compared to the existing HDC.