Efficient Design of a Hyperdimensional Processing Unit for Multi-Layer Cognition

The methodology used to design and optimize the very first general-purpose hyperdimensional (HD) processing unit capable of executing a broad spectrum of HD workloads (called "HPU") is presented. HD computing is a brain-inspired computational paradigm that uses the principles of high-dimensional mathematics to perform cognitive tasks. While considerable efforts have been spent toward realizing efficient HD processors, all of these targeted specific application domains, most often pattern classification. In contrast, the HPU design addresses the multiple layers of a cognitive process. A structured methodology identifies the kernel HD computations recurring at each of these layers, and maps them onto a unified and parameterized architectural model. The effectiveness in terms of runtime and energy consumption of the approach is evaluated. The results show that the resulting HPU efficiently processes the full range of HD algorithms, and far outperforms baseline implementations on a GPU.