Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A C Code Generator for Fast Inference and Simple Deployment of
  Convolutional Neural Networks on Resource Constrained Systems   Inference of Convolutional Neural Networks in time critical applications
usually requires a GPU. In robotics or embedded devices these are often not
available due to energy, space and cost constraints. Furthermore, installation
of a deep learning framework or even a native compiler on the target platform
is not possible. This paper presents a neural network code generator (NNCG)
that generates from a trained CNN a plain ANSI C code file that encapsulates
the inference in single a function. It can easily be included in existing
projects and due to lack of dependencies, cross compilation is usually
possible. Additionally, the code generation is optimized based on the known
trained CNN and target platform following four design principles. The system is
evaluated utilizing small CNN designed for this application. Compared to
TensorFlow XLA and Glow speed-ups of up to 11.81 can be shown and even GPUs are
outperformed regarding latency.

21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat