EventHD: Robust and efficient hyperdimensional learning with neuromorphic sensor - Zou, Zhuowen; Alimohamadi, Haleh; Kim, Yeseong; Najafi, M. Hassan; Srinivasa, Narayan; Imani, Mohsen (Frontiers in Neuroscience, 2022)
Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Hyper-Dimensional Computing (HDC) has shown promising results in enabling efficient and robust cognitive learning. In this study, we exploit HDC as an alternative computational model that mimics important brain functionalities toward high-efficiency and noise-tolerant neuromorphic computing. We present <sans-serif>EventHD</sans-serif>, an end-to-end learning framework based on HDC for robust, efficient learning from neuromorphic sensors. We first introduce a spatial and temporal encoding scheme to map event-based neuromorphic data into high-dimensional space. Then, we leverage HDC mathematics to support learning and cognitive tasks over encoded data, such as information association and memorization. <sans-serif>EventHD</sans-serif> also provides a notion of confidence for each prediction, thus enabling self-learning from unlabeled data. We evaluate <sans-serif>EventHD</sans-serif> efficiency over data collected from Dynamic Vision Sensor (DVS) sensors. Our results indicate that <sans-serif>EventHD</sans-serif> can provide online learning and cognitive support while operating over raw DVS data without using the costly preprocessing step. In terms of efficiency, <sans-serif>EventHD</sans-serif> provides 14.2× faster and 19.8× higher energy efficiency than state-of-the-art learning algorithms while improving the computational robustness by 5.9×.