The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks compared to the widely used deep learning-based approaches. Spatio-temporal (ST) signal processing, which encompasses biosignals such as electromyography (EMG) and electroencephalography (EEG), is one family of applications that could benefit from an HDC-based learning framework. At the core of HDC lie manipulations and comparisons of large bit patterns, which are inherently ill-suited to conventional computing platforms based on the von-Neumann architecture. In this work, we propose an architecture for ST signal processing within the HDC framework using predominantly in-memory compute arrays. In particular, we introduce a methodology for the in-memory hyperdimensional encoding of ST data to be used together with an in-memory associative search module. We show that the in-memory HDC encoder for ST signals offers at least $1.80\times $ energy efficiency gains, $3.36\times $ area gains, as well as $9.74\times $ throughput gains compared with a dedicated digital hardware implementation. At the same time it achieves a peak classification accuracy within 0.04% of that of the baseline HDC framework.

中文翻译：

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时空信号处理的高效节能内存中超维编码

与广泛使用的基于深度学习的方法相比，已被证明的新兴的大脑启发式计算范例超维计算（HDC）可为各种认知任务提供轻量级的学习框架。时空（ST）信号处理包括生物信号，例如肌电图（EMG）和脑电图（EEG），是可以从基于HDC的学习框架中受益的一系列应用程序。HDC的核心在于对大位模式的操作和比较，这本来就不适合基于von-Neumann架构的常规计算平台。在这项工作中，我们提出了一种主要使用内存中的计算阵列在HDC框架内进行ST信号处理的体系结构。特别是，我们介绍了一种用于ST数据的内存超维编码的方法，该方法将与内存内关联搜索模块一起使用。我们表明，用于ST信号的内存HDC编码器至少可提供 $ 1.80 \次$ 能源效率的提高， $ 3.36 \次$ 面积增加，以及 $ 9.74 \次$ 与专用数字硬件实现相比，吞吐量提高了。同时，它实现的峰值分类精度在基准HDC框架的0.04％之内。