<p> Designing self-regulating machines that can see and
comprehend various real world objects around it are the main purpose of the AI
domain. Recently, there has been marked
advancements in the field of deep learning to create state-of-the-art DNNs for
various CV applications. It is
challenging to deploy these DNNs into resource-constrained micro-controller
units as often they are quite memory intensive. Design Space Exploration is a technique which makes CNN/DNN memory
efficient and more flexible to be deployed into resource-constrained
hardware. MobileNet is small DNN architecture
which was designed for embedded and mobile vision, but still researchers faced
many challenges in deploying this model into resource limited real-time processors.</p><p> This thesis, proposes three new DNN architectures, which are
developed using the Design Space Exploration technique. The state-of-the art
MobileNet baseline architecture is used as foundation to propose these DNN architectures
in this study. They are enhanced versions of the baseline MobileNet
architecture. DSE techniques like data augmentation, architecture tuning, and architecture
modification have been done to improve the baseline architecture. First, the
Thin MobileNet architecture is proposed which uses more intricate block modules
as compared to the baseline MobileNet. It is a compact, efficient and flexible
architecture with good model accuracy. To get a more compact models, the
KilobyteNet and the Ultra-thin MobileNet DNN architecture is proposed.
Interesting techniques like channel depth alteration and hyperparameter tuning
are introduced along-with some of the techniques used for designing the Thin
MobileNet. All the models are trained and validated from scratch on the CIFAR-10 dataset. The experimental results (training and testing) can be visualized using the live accuracy and logloss graphs provided by the Liveloss package. The Ultra-thin MobileNet model is more balanced in terms of the model accuracy and model size out of the three and hence it is deployed into the NXP i.MX RT1060 embedded hardware unit for image classification application.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12195342 |
| Date | 28 April 2020 |
| Creators | DEBJYOTI SINHA (8764737) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Design_Space_Exploration_of_MobileNet_for_Suitable_Hardware_Deployment/12195342 |
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