The use of artificial neural networks for object detection, along with advancements in 6G and
IoT research, plays an important role in applications such as drone-based monitoring of
structures, search and rescue operations, and deployment on hardware platforms like
FPGAs. However, a key challenge in implementing these networks on such hardware is the
need to economize computational resources. Despite substantial advances in computational
capacity, implementing devices with ample resources remains challenging. As a solution,
techniques for partitioning and compressing neural networks, as well as compressing
activation signals (or feature maps), have been developed. This work proposes a system
that partitions neural network models for object detection in videos, allocating part of the
network to an end device and the remainder to a cloud server. The system also compresses
the feature maps generated by the last layers on the end device by exploiting temporal
correlation, enabling a predictive compression scheme. This approach allows neural
networks to be embedded in low-power devices while respecting the computational limits of
the device, the transmission rate constraints of the communication channel between the
device and server, and the network’s accuracy requirements. Experiments conducted on
pre-trained neural network models show that the proposed system can significantly reduce
the amount of data to be stored or transmitted by leveraging temporal correlation, facilitating
the deployment of these networks on devices with limited computational power
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