<p>Machine
learning is a rapidly developing field in computer research. Deep
neural network architectures such as Resnet have allowed computers to
process unstructured data such as images and videos with an extremely
high degree of accuracy while at the same time managing to deliver
those results with a reasonably low amount of latency. However,
while deep neural networks are capable of achieving very impressive
results, they are still very memory and computationally intensive,
limiting their use to clusters with significant amounts of resources.
This paper examines the possibility of running deep neural networks
on mobile hardware, platforms with much more limited memory and
computational bandwidth. We first examine the limitations of a
mobile platform and what steps have to be taken to overcome those
limitations in order to allow a deep neural network to operate on a
mobile device with a reasonable level of performance. We then
proceed into an examination of ApproxNet, a neural network designed
to be run on mobile devices. ApproxNet provides a demonstration of
how mobile hardware limits the performance of deep neural networks
while also showing that these issues can be to an extent overcome,
allowing a neural network to maintain usable levels of latency and
accuracy.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9455021 |
| Date | 16 October 2019 |
| Creators | Peter Bai (7181939) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Operating_Neural_Networks_on_Mobile_Devices/9455021 |
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