  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.
