  Deep learning applications have achieved great success in numerous real-world
applications. Deep learning models, especially Convolution Neural Networks
(CNN) are often prototyped using FPGA because it offers high power efficiency
and reconfigurability. The deployment of CNNs on FPGAs follows a design cycle
that requires saving of model parameters in the on-chip memory during
High-level synthesis (HLS). Recent advances in edge intelligence require CNN
inference on edge network to increase throughput and reduce latency. To provide
flexibility, dynamic parameter allocation to different mobile devices is
required to implement either a predefined or defined on-the-fly CNN
architecture. In this study, we present novel methodologies for dynamically
streaming the model parameters at run-time to implement a traditional CNN
architecture. We further propose a library-based approach to design scalable
and dynamic distributed CNN inference on the fly leveraging
partial-reconfiguration techniques, which is particularly suitable for
resource-constrained edge devices. The proposed techniques are implemented on
the Xilinx PYNQ-Z2 board to prove the concept by utilizing the LeNet-5 CNN
model. The results show that the proposed methodologies are effective, with
classification accuracy rates of 92%, 86%, and 94% respectively
