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By mimicking brain-like cognition and exploiting parallelism,hyperdimensional computing (HDC) classifiers have been emerging as alightweight framework to achieve efficient on-device inference. Nonetheless,they have two fundamental drawbacks, heuristic training process and ultra-highdimension, which result in sub-optimal inference accuracy and large model sizesbeyond the capability of tiny devices with stringent resource constraints. Inthis paper, we address these fundamental drawbacks and propose alow-dimensional computing (LDC) alternative. Specifically, by mapping our LDCclassifier into an equivalent neural network, we optimize our model using aprincipled training approach. Most importantly, we can improve the inferenceaccuracy while successfully reducing the ultra-high dimension of existing HDCmodels by orders of magnitude (e.g., 8000 vs. 4/64). We run experiments toevaluate our LDC classifier by considering different datasets for inference ontiny devices, and also implement different models on an FPGA platform foracceleration. The results highlight that our LDC classifier offers anoverwhelming advantage over the existing brain-inspired HDC models and isparticularly suitable for inference on tiny devices.

A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices