Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Brain-Inspired Low-Dimensional Computing Classifier for Inference on
  Tiny Devices   By mimicking brain-like cognition and exploiting parallelism,
hyperdimensional computing (HDC) classifiers have been emerging as a
lightweight framework to achieve efficient on-device inference. Nonetheless,
they have two fundamental drawbacks, heuristic training process and ultra-high
dimension, which result in sub-optimal inference accuracy and large model sizes
beyond the capability of tiny devices with stringent resource constraints. In
this paper, we address these fundamental drawbacks and propose a
low-dimensional computing (LDC) alternative. Specifically, by mapping our LDC
classifier into an equivalent neural network, we optimize our model using a
principled training approach. Most importantly, we can improve the inference
accuracy while successfully reducing the ultra-high dimension of existing HDC
models by orders of magnitude (e.g., 8000 vs. 4/64). We run experiments to
evaluate our LDC classifier by considering different datasets for inference on
tiny devices, and also implement different models on an FPGA platform for
acceleration. The results highlight that our LDC classifier offers an
overwhelming advantage over the existing brain-inspired HDC models and is
particularly suitable for inference on tiny devices.

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The categories of this paper are: q-alg
9
The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: adap-org