| Abstract: | Deep learning technology has been widely used in computer vision, speech
recognition, natural language processing, and other related fields. The deep learning
algorithm has high precision and high reliability. However, the lack of resources in the edge
terminal equipment makes it difficult to run deep learning algorithms that require more
memory and computing power. In this paper, we propose MoTransFrame, a general model
processing framework for deep learning models. Instead of designing a model compression
algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional
neural networks models to resources-starved edge devices promptly and accurately. By the
integration method, Deep learning models can be converted into portable projects for Arduino,
a typical edge device with limited resources. Our experiments show that MoTransFrame has
good adaptability in edge devices with limited memories. It is more flexible than other model
transplantation methods. It can keep a small loss of model accuracy when the number of
parameters is compressed by tens of times. At the same time, the computational resources
needed in the reasoning process are less than what the edge node could handle. |
