Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fixed-point optimization of deep neural networks with adaptive step size
  retraining   Fixed-point optimization of deep neural networks plays an important role in
hardware based design and low-power implementations. Many deep neural networks
show fairly good performance even with 2- or 3-bit precision when quantized
weights are fine-tuned by retraining. We propose an improved fixedpoint
optimization algorithm that estimates the quantization step size dynamically
during the retraining. In addition, a gradual quantization scheme is also
tested, which sequentially applies fixed-point optimizations from high- to
low-precision. The experiments are conducted for feed-forward deep neural
networks (FFDNNs), convolutional neural networks (CNNs), and recurrent neural
networks (RNNs).

2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
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The categories of this paper are: physics
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc