  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.
