Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimizing Convolutional Neural Networks for Embedded Systems by Means
  of Neuroevolution   Automated design methods for convolutional neural networks (CNNs) have
recently been developed in order to increase the design productivity. We
propose a neuroevolution method capable of evolving and optimizing CNNs with
respect to the classification error and CNN complexity (expressed as the number
of tunable CNN parameters), in which the inference phase can partly be executed
using fixed point operations to further reduce power consumption. Experimental
results are obtained with TinyDNN framework and presented using two common
image classification benchmark problems -- MNIST and CIFAR-10.

8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio