HyperDyn: Dynamic Dimensional Masking for Efficient Hyper-Dimensional Computing

Hyper-dimensional computing (HDC) is a bio-inspired computing paradigm that mimics cognitive tasks by encoding data into high-dimensional vectors and employing non-complex learning techniques. However, existing HDC solutions face a major challenge hindering their deployment on low-power embedded devices: the costly associative search module, especially in high-precision computations. This module involves calculating the distance between class vectors and query vectors, as well as sorting distances. In this paper, we present HyperDyn, an efficient dynamic inference framework designed for accurate and efficient hyper-dimensional computing. Our framework first offline analyzes the importance of different dimensions in the associative memory based on the contributions of the dimensions to the classification accuracy. In addition, we introduce a dynamic dimensional importance scaling mechanism for more flexible and accurate dimension contribution judgments. Finally, HyperDyn achieves efficient dynamic associative search through a dimension masking mechanism that adapts to the characteristics of the input sample. We evaluate HyperDyn on datasets from three different fields and the results show that HyperDyn can achieve 7.65 × speedup and 58% energy savings, with less than 0.2% loss in accuracy.