Scalable edge-based hyperdimensional learning system with brain-like neural adaptation

Abstract

In the Internet of Things (IoT) domain, many applications are running machine learning algorithms to assimilate the data collected in the swarm of devices. Sending all data to the powerful computing environment, e.g., cloud, poses significant efficiency and scalability issues. A promising way is to distribute the learning tasks onto the IoT hierarchy, often referred to edge computing; however, the existing sophisticated algorithms such as deep learning are often overcomplex to run on less-powerful and unreliable embedded IoT devices. Hyperdimensional Computing (HDC) is a brain-inspired learning approach for efficient and robust learning on today's embedded devices. Encoding, or transforming the input data into high-dimensional representation, is the key first step of HDC before performing a learning task. All existing HDC approaches use a static encoder; thus, they still require very high dimensionality, resulting in significant efficiency loss for the edge devices with limited resources. In this paper, we have developed NeuralHD, a new HDC approach with a dynamic encoder for adaptive learning. Inspired by human neural regeneration study in neuroscience, NeuralHD identifies insignificant dimensions and regenerates those dimensions to enhance the learning capability and robustness. We also present a scalable learning framework to distribute NeuralHD computation over edge devices in IoT systems. Our solution enables edge devices capable of real-time learning from both labeled and unlabeled data. Our evaluation on a wide range of practical classification tasks shows that NeuralHD provides 5.7X and 6.1X (12.3X and 14.1X) faster and more energy-efficient training compared to the HD-based algorithms (DNNs) running on the same platform. NeuralHD also provides 4.2X and 11.6X higher robustness to noise in the unreliable network and hardware of IoT environments as compared to DNNs.