Integrating Branching and Pruning for Efficient Hyperdimensional Computing

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.