Optimal Hyperdimensional Representation for Learning and Cognitive Computation

Prathyush Poduval, Hamza Errahmouni Barkam, Xiangjian Liu, Sanggeon Yun, Yang Ni, Zhuowen Zou, Mohsen Imani

Keywords: hyperdimensional computing; vector symbolic architecture; decoding;

Abstract:

Hyperdimensional Computing (HDC), as a novel neurally-inspired computing methodology, uses lightweight and high-dimensional operations to realize major brain functionalities. Recent HDC works mainly focus on two aspects: brain-like learning and cognitive computation. However, it lacks differentiation between these functions and their requirements for HDC algorithms. We address this gap by proposing an adaptable hyperdimensional kernel-based encoding method. We explore how encoding settings impact HDC performance for both tasks, highlighting the distinction between learning patterns and retrieving information. We provide detailed guidance on kernel design, optimizing data points for accurate decoding or correlated learning. Experimental results with our proposed encoder significantly boost image classification accuracy from 65% to 95% by considering pixel correlations and increase decoding accuracy from 85% to 100% by maximizing pixel vector separation. Factorization tasks are shown to require highly exclusive representation to enable accurate convergence.

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

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Submission Number: 12307