Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixed-Precision Neural Network Quantization via Learned Layer-wise
  Importance   The exponentially large discrete search space in mixed-precision quantization
(MPQ) makes it hard to determine the optimal bit-width for each layer. Previous
works usually resort to iterative search methods on the training set, which
consume hundreds or even thousands of GPU-hours. In this study, we reveal that
some unique learnable parameters in quantization, namely the scale factors in
the quantizer, can serve as importance indicators of a layer, reflecting the
contribution of that layer to the final accuracy at certain bit-widths. These
importance indicators naturally perceive the numerical transformation during
quantization-aware training, which can precisely and correctly provide
quantization sensitivity metrics of layers. However, a deep network always
contains hundreds of such indicators, and training them one by one would lead
to an excessive time cost. To overcome this issue, we propose a joint training
scheme that can obtain all indicators at once. It considerably speeds up the
indicators training process by parallelizing the original sequential training
processes. With these learned importance indicators, we formulate the MPQ
search problem as a one-time integer linear programming (ILP) problem. That
avoids the iterative search and significantly reduces search time without
limiting the bit-width search space. For example, MPQ search on ResNet18 with
our indicators takes only 0.06 seconds. Also, extensive experiments show our
approach can achieve SOTA accuracy on ImageNet for far-ranging models with
various constraints (e.g., BitOps, compress rate).

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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: econ
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The categories of this paper are: acc-phys