The Internet of Things (IoT) has facilitated many applications utilizing edge-based machine learning (ML) methods to analyze locally collected data. Unfortunately, popular ML algorithms often require intensive computations beyond the capabilities of today's IoT devices. Brain-inspired hyperdimensional computing (HDC) has been introduced to address this issue. However, existing HDCs use static encoders, requiring extremely high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a huge efficiency loss, severely impeding the application of HDCs in IoT systems. We observed that a main cause is that the encoding module of existing HDCs lacks the capability to utilize and adapt to information learned during training. In contrast, neurons in human brains dynamically regenerate all the time and provide more useful functionalities when learning new information. While the goal of HDC is to exploit the high-dimensionality of randomly generated base hypervectors to represent the information as a pattern of neural activity, it remains challenging for existing HDCs to support a similar behavior as brain neural regeneration. In this work, we present dynamic HDC learning frameworks that identify and regenerate undesired dimensions to provide adequate accuracy with significantly lowered dimensionalities, thereby accelerating both the training and inference.

翻译：物联网（IoT）推动了利用边缘机器学习方法分析本地采集数据的诸多应用。然而，流行的机器学习算法通常需要远超当前物联网设备能力的密集计算。受大脑启发的超维计算（HDC）被引入以解决这一问题。但现有HDC使用静态编码器，需极高维度及数百次训练迭代才能达到合理精度，导致巨大效率损失，严重阻碍HDC在物联网系统中的应用。我们发现主要原因是现有HDC的编码模块缺乏利用和适应训练过程中所学信息的能力。相比之下，人类大脑中的神经元会持续动态再生，并在学习新信息时提供更有用的功能。虽然HDC的目标是利用随机生成的基础超向量的高维性将信息表示为神经活动模式，但现有HDC仍难以支持类似大脑神经再生的行为。本研究提出动态HDC学习框架，通过识别并再生低效维度，在显著降低维度的同时保证足够精度，从而加速训练与推理过程。