Primitive-Driven Acceleration of Hyperdimensional Computing for Real-Time Image Classification

Hyperdimensional Computing (HDC) represents data using extremely high-dimensional, low-precision vectors— termed hypervectors (HVs)—and performs learning and inference through lightweight, noise-tolerant operations. However, the high dimensionality, sparsity, and repeated data movement involved in HDC make these computations difficult to accelerate efficiently on conventional processors. As a result, executing core HDC operations—binding, permutation, bundling, and similarity search—on CPUs or GPUs often leads to suboptimal utilization, memory bottlenecks, and limits on real-time performance.In this paper, our contributions are two-fold. First, we develop an image-encoding algorithm that, similar in spirit to convolutional neural networks, maps local image patches to hypervectors enriched with spatial information. These patch-level hypervectors are then merged into a global representation using the fundamental HDC operations, enabling spatially sensitive and robust image encoding. This encoder achieves 95.67% accuracy on MNIST and 85.14% on Fashion-MNIST, outperforming prior HDC-based image encoders. Second, we design an end-to-end accelerator that implements these compute operations on an FPGA through a pipelined architecture that exploits parallelism both across the hypervector dimensionality and across the set of image patches. Our Alveo U280 implementation delivers 0.09ms inference latency, achieving up to 1300× and 60× speedup over state-of-the-art CPU and GPU baselines, respectively.