Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Deep Convolutional Neural Networks for the
  Internet-of-Things   Severe constraints on memory and computation characterizing the
Internet-of-Things (IoT) units may prevent the execution of Deep Learning
(DL)-based solutions, which typically demand large memory and high processing
load. In order to support a real-time execution of the considered DL model at
the IoT unit level, DL solutions must be designed having in mind constraints on
memory and processing capability exposed by the chosen IoT technology. In this
paper, we introduce a design methodology aiming at allocating the execution of
Convolutional Neural Networks (CNNs) on a distributed IoT application. Such a
methodology is formalized as an optimization problem where the latency between
the data-gathering phase and the subsequent decision-making one is minimized,
within the given constraints on memory and processing load at the units level.
The methodology supports multiple sources of data as well as multiple CNNs in
execution on the same IoT system allowing the design of CNN-based applications
demanding autonomy, low decision-latency, and high Quality-of-Service.

5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph