In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human–computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of > 76% for valence and > 73% for arousal on the multi-modal AMIGOS and DEAP data sets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.

中文翻译：

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使用具有组合通道编码和元胞自动机的超维计算进行高效情绪识别

在本文中，提出了一种基于高效类脑超维计算 (HDC) 范式的情感识别硬件优化方法。情感识别为人机交互提供了有价值的信息；然而，从记忆的角度来看，情绪识别中涉及的大量输入通道（> 200）和模态（> 3）非常昂贵。为了解决这个问题，提出了内存减少和优化的方法，包括一种利用编码过程的组合特性的新方法，以及一个基本的元胞自动机。具有早期传感器融合的 HDC 与所提出的技术一起实施，在多模态 AMIGOS 和 DEAP 数据集上实现效价 > 76% 和唤醒度 > 73% 的两类多模态分类精度，几乎总是比最先进的更好。所需的向量存储无缝减少了 98%，向量请求的频率至少减少了 1/5。结果证明了高效的超维计算在低功耗、多通道情绪识别任务中的潜力。