Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantization and Training of Neural Networks for Efficient
  Integer-Arithmetic-Only Inference   The rising popularity of intelligent mobile devices and the daunting
computational cost of deep learning-based models call for efficient and
accurate on-device inference schemes. We propose a quantization scheme that
allows inference to be carried out using integer-only arithmetic, which can be
implemented more efficiently than floating point inference on commonly
available integer-only hardware. We also co-design a training procedure to
preserve end-to-end model accuracy post quantization. As a result, the proposed
quantization scheme improves the tradeoff between accuracy and on-device
latency. The improvements are significant even on MobileNets, a model family
known for run-time efficiency, and are demonstrated in ImageNet classification
and COCO detection on popular CPUs.

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The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn