论文标题
高维计算与神经网络：比较体系结构和学习过程
Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process
论文作者
论文摘要
超维计算（HDC）作为新兴的非von Neumann计算范式获得了广泛的关注。 HDC受到人脑功能功能的方式的启发，利用高维模式来执行学习任务。与神经网络相比，HDC显示出优势，例如能源效率和较小的模型大小，但在复杂应用中的PAR学习能力低于PAR。最近，研究人员观察到与神经网络组件相结合时，HDC可以比传统的HDC模型获得更好的性能。这激发了我们探索HDC理论基础背后的更深入的见解，尤其是与神经网络的联系和差异。在本文中，我们在HDC和神经网络之间进行了比较研究，以提供不同的角度，其中HDC可以从经过预先训练的非常紧凑的神经网络中得出。实验结果表明，这种神经网络衍生的HDC模型可以分别提高传统和基于学习的HDC模型的准确度高达21％和5％。本文旨在为未来的方向提供更多的见解和灯光，以研究这种流行的新兴学习计划。
Hyperdimensional Computing (HDC) has obtained abundant attention as an emerging non von Neumann computing paradigm. Inspired by the way human brain functions, HDC leverages high dimensional patterns to perform learning tasks. Compared to neural networks, HDC has shown advantages such as energy efficiency and smaller model size, but sub-par learning capabilities in sophisticated applications. Recently, researchers have observed when combined with neural network components, HDC can achieve better performance than conventional HDC models. This motivates us to explore the deeper insights behind theoretical foundations of HDC, particularly the connection and differences with neural networks. In this paper, we make a comparative study between HDC and neural network to provide a different angle where HDC can be derived from an extremely compact neural network trained upfront. Experimental results show such neural network-derived HDC model can achieve up to 21% and 5% accuracy increase from conventional and learning-based HDC models respectively. This paper aims to provide more insights and shed lights on future directions for researches on this popular emerging learning scheme.
