Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantization-Guided Training for Compact TinyML Models   We propose a Quantization Guided Training (QGT) method to guide DNN training
towards optimized low-bit-precision targets and reach extreme compression
levels below 8-bit precision. Unlike standard quantization-aware training (QAT)
approaches, QGT uses customized regularization to encourage weight values
towards a distribution that maximizes accuracy while reducing quantization
errors. One of the main benefits of this approach is the ability to identify
compression bottlenecks. We validate QGT using state-of-the-art model
architectures on vision datasets. We also demonstrate the effectiveness of QGT
with an 81KB tiny model for person detection down to 2-bit precision
(representing 17.7x size reduction), while maintaining an accuracy drop of only
3% compared to a floating-point baseline.

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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg