Efficient Hyperdimensional Learning with Trainable, Quantizable, and Holistic Data Representation

Abstract

Hyperdimensional computing (HDC) is a computing paradigm that draws inspiration from human memory models. It represents data in the form of high-dimensional vectors. Recently, many works in literature have tried to use HDC as a learning model due to its simple arithmetic and high efficiency. However, learning frameworks in HDC use encoders that are randomly generated and static, resulting in many parameters and low accuracy. In this paper, we propose TrainableHD, a framework for HDC that utilizes a dynamic encoder with effective quantization for higher efficiency. Our model considers errors gained from the HD model and dynamically updates the encoder during training. Our evaluations show that TrainableHD improves the accuracy of the HDC by up to 22.26% (on average 3.62%) without any extra computation costs, achieving a comparable level to state-of-the-art deep learning. Also, the proposed solution is 56.4 x faster and 73 x more energy efficient as compared to the deep learning on NVIDIA Jetson Xavier, a low-power GPU platform.