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Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata
arXiv - CS - Emerging Technologies Pub Date : 2021-04-06 , DOI: arxiv-2104.02804
Alisha Menon, Anirudh Natarajan, Reva Agashe, Daniel Sun, Melvin Aristio, Harrison Liew, Yakun Sophia Shao, Jan M. Rabaey

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 datasets, 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:

使用超多维计算结合组合通道编码和细胞自动机进行有效的情感识别

在本文中,提出了一种基于有效的脑启发式超维计算(HDC)范例的硬件优化的情绪识别方法。情绪识别为人机交互提供了有价值的信息,但是从内存的角度来看,涉及情绪识别的大量输入通道(> 200)和模态(> 3)非常昂贵。为了解决这个问题,提出了用于存储器减少和优化的方法,包括利用编码过程的组合性质的新颖方法以及基本的细胞自动机。结合早期传感器融合技术的HDC与提议的技术一起实现,在多模态AMIGOS和DEAP数据集上,两价多模态分类准确度的价数> 76%,唤醒时的准确度> 73%,几乎总是比最新技术要好。所需的向量存储无缝地减少了98%,向量请求的频率至少减少了1/5。结果证明了针对低功率,多通道情感识别任务的高效超维计算的潜力。
更新日期:2021-04-08
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