Please use this identifier to cite or link to this item:
http://oaps.umac.mo/handle/10692.1/250
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | CHENG, OON FUNG(鄭渙楓) | - |
dc.contributor.author | XIN, GUO QIANG(辛國強) | - |
dc.date.accessioned | 2021-07-05T09:21:37Z | - |
dc.date.available | 2021-07-05T09:21:37Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Cheng, O. F., Xin, G. Q. (2021). Machine Learning Based Edge AI for IoT (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository. | en_US |
dc.identifier.uri | http://oaps.umac.mo/handle/10692.1/250 | - |
dc.description.abstract | With the further development of deep neural network, more and more large-scale network structures are proposed, the large-scale network led to the increase in the number of parameters, and the amount of calculation. This will challenge devices’ hardware and greatly increase the power consumption [1]. So, it is unfavorable for using large-scale network on terminal devices. We will solve these problems through design special network structure and quantize the weight and activation function in our design. Make the large-scale network can be better used on the terminal equipment. The design is based on the ResNet structure [2] and used groups of convolutions [3] with both binarized weights and activations [4] to build lightweight deep neural networks. During the forward pass, our design drastically reduces memory size and accesses, and replace most arithmetic operations with bit-wise operations to simplify the computation and reduce memory size, which is expected to substantially improve power-efficiency and Computing speed. We designed a baseline and continued to adjust the network architecture and parameters for better tradeoff between latency and accuracy. Finally, we got satisfied results on CIFAR-10 dataset [5]. At last, we will compare the network we designed with state-of-the-art network in the Cifar-10 100k benchmark [6] to show the performance of our design. | en_US |
dc.language.iso | en | en_US |
dc.title | Machine Learning Based Edge AI for IoT | en_US |
dc.type | OAPS | en_US |
dc.contributor.department | Department of Electrical and Computer Engineering | en_US |
dc.description.instructor | Prof. Dr. Yu Wei Han, Prof. Dr. Un Ka Fai | en_US |
dc.contributor.faculty | Faculty of Science and Technology | en_US |
dc.description.course | Bachelor of Science in Electrical and Computer Engineering | en_US |
dc.description.programme | Bachelor of Science in Electrical and Computer Engineering | en_US |
Appears in Collections: | FST OAPS 2021 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
OAPS_2021_FST_DB602053_DB727082_Cheng OonFung_Xin GuoQiang_Machine Learning Based Edge AI for IoT.pdf | 25.31 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.