L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks

Abstract

Brain-inspired hyperdimensional computing (HDC) has been introduced as an alternative computing paradigm to achieve efficient and robust learning. HDC simulates cognitive tasks by mapping all data points to patterns of neural activity in the high-dimensional space, which has demonstrated promising performances in a wide range of applications such as robotics, biomedical signal processing, and genome sequencing. Language tasks, generally solved using machine learning methods, are widely deployed on low-power embedded devices. However, existing HDC solutions suffer from major challenges that impede the deployment of low-power embedded devices: the storage and computation overhead of HDC models grows dramatically with (i) the number of dimensions and (ii) the complex similarity metric during the inference.