Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance tuning for deep learning on a many-core processor (master
  thesis)   Convolutional neural networks (CNNs) are becoming very successful and popular
for a variety of applications. The Loki many-core processor architecture is
very promising for achieving specialised hardware performance and efficiency
while being a general purpose solution. Loki combines many simple cores with
increased control for the programmer. This freedom can be exploited to produce
much more efficient code than in conventional multiprocessors but it also
creates a very big design space for possible optimisations. In this project, I
explore possible optimisations for a CNN application, their portability on
different Loki-specific configurations, convolution parameters and inputs.
Finally, I investigate the potential for adaptive algorithms for further
performance increase.

21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci