Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things
This addresses the challenge of efficient edge intelligence for IoT applications, though it appears incremental as it builds on existing partitioning and pipelining techniques.
The paper tackles the problem of deploying Convolutional Neural Networks on resource-constrained IoT devices by proposing a dynamic partitioning and pipelining method that distributes computation horizontally, resulting in a throughput increase of 1.55x to 1.75x without sacrificing accuracy.
In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices. By partitioning and pipelining the CNN, it horizontally distributes the computation load among resource-constrained devices (called horizontal collaboration), which in turn increases the throughput. Through partitioning, we can decrease the computation and energy consumption on individual IoT devices and increase the throughput without sacrificing accuracy. Also, by processing the data at the generation point, data privacy can be achieved. The results show that throughput can be increased by 1.55x to 1.75x for sharing the CNN into two and three resource-constrained devices, respectively.