Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computationally minimal algorithm is dominated by costly hypervector memory storage, which grows linearly with the number of sensors. To address this, the memory is replaced with a light-weight cellular automaton for on-the-fly hypervector generation. The use of this technique is explored in conjunction with vector folding for various real-time classification latencies in post-layout simulation on an emotion recognition dataset with 200 channels. The proposed architecture achieves 39.1 nJ/prediction; a 4.9× energy efficiency improvement, 9.5× per channel, over the state-of-the-art HDC processor. At maximum throughput, the architecture achieves a 10.7× improvement, 33.5× per channel. An optimized support vector machine (SVM) processor is designed in this work for the same use-case. HDC is 9.5× more energy-efficient than the SVM, paving the way for it to become the paradigm of choice for high-accuracy, on-board biosignal classification.