GENERIC: highly efficient learning engine on edge using hyperdimensional computing

Behnam Khaleghi, Jaeyoung Kang, Hanyang Xu, Justin Morris, Tajana Rosing

Abstract: Hyperdimensional Computing (HDC) mimics the brain's basic principles in performing cognitive tasks by encoding the data to high-dimensional vectors and employing non-complex learning techniques. Conventional processing platforms such as CPUs and GPUs are incapable of taking full advantage of the highly-parallel bit-level operations of HDC. On the other hand, existing HDC encoding techniques do not cover a broad range of applications to make a custom design plausible. In this paper, we first propose a novel encoding that achieves high accuracy for diverse applications. Thereafter, we leverage the proposed encoding and design a highly efficient and flexible ASIC accelerator, dubbed GENERIC, suited for the edge domain. GENERIC supports both classification (train and inference) and clustering for unsupervised learning on edge. Our design is flexible in the input size (hence it can run various applications) and hypervectors dimensionality, allowing it to trade off the accuracy and energy/performance on-demand. We augment GENERIC with application-opportunistic power-gating and voltage over-scaling (thanks to the notable error resiliency of HDC) for further energy reduction. GENERIC encoding improves the prediction accuracy over previous HDC and ML techniques by 3.5% and 6.5%, respectively. At 14 nm technology node, GENERIC occupies an area of 0.30 mm2, and consumes 0.09 mW static and 1.97 mW active power. Compared to the previous inference-only accelerator, GENERIC reduces the energy consumption by 4.1×.