Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enabling Embedded Inference Engine with ARM Compute Library: A Case
  Study   When you need to enable deep learning on low-cost embedded SoCs, is it better
to port an existing deep learning framework or should you build one from
scratch? In this paper, we share our practical experiences of building an
embedded inference engine using ARM Compute Library (ACL). The results show
that, contradictory to conventional wisdoms, for simple models, it takes much
less development time to build an inference engine from scratch compared to
porting existing frameworks. In addition, by utilizing ACL, we managed to build
an inference engine that outperforms TensorFlow by 25%. Our conclusion is that,
on embedded devices, we most likely will use very simple deep learning models
for inference, and with well-developed building blocks such as ACL, it may be
better in both performance and development time to build the engine from
scratch.

37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys