Efficient Hyperdimensional Computing with Modular Composite Representations

TL;DR

This paper introduces Modular Composite Representation (MCR), a high-dimensional computing model that offers improved capacity, accuracy, and hardware efficiency over existing models, supported by extensive evaluation and a dedicated accelerator.

Contribution

It provides the first comprehensive evaluation of MCR, demonstrating its advantages in capacity, accuracy, and hardware implementation, including a novel dedicated accelerator design.

Findings

MCR outperforms binary and integer vectors in capacity.

MCR matches binary spatter code performance with 4x less memory.

Hardware implementation achieves up to 3 orders of magnitude speedup.

Abstract

The modular composite representation (MCR) is a computing model that represents information with high-dimensional integer vectors using modular arithmetic. Originally proposed as a generalization of the binary spatter code model, it aims to provide higher representational power while remaining a lighter alternative to models requiring high-precision components. Despite this potential, MCR has received limited attention. Systematic analyses of its trade-offs and comparisons with other models are lacking, sustaining the perception that its added complexity outweighs the improved expressivity. In this work, we revisit MCR by presenting its first extensive evaluation, demonstrating that it achieves a unique balance of capacity, accuracy, and hardware efficiency. Experiments measuring capacity demonstrate that MCR outperforms binary and integer vectors while approaching complex-valued representations at a fraction of their memory footprint. Evaluation on 123 datasets confirms consistent accuracy gains and shows that MCR can match the performance of binary spatter codes using up to 4x less memory. We investigate the hardware realization of MCR by showing that it maps naturally to digital logic and by designing the first dedicated accelerator. Evaluations on basic operations and 7 selected datasets demonstrate a speedup of up to 3 orders of magnitude and significant energy reductions compared to software implementation. When matched for accuracy against binary spatter codes, MCR achieves on average 3.08x faster execution and 2.68x lower energy consumption. These findings demonstrate that, although MCR requires more sophisticated operations than binary spatter codes, its modular arithmetic and higher per-component precision enable lower dimensionality. When realized with dedicated hardware, this results in a faster, more energy-efficient, and high-precision alternative to existing models.