Modern Convolutional Neural Networks (CNNs) are known to be computationally and memory costly owing to the deep structure that is constantly growing. A reconfigurable design is crucial in tackling this difficulty since neural network requirements are always evolving. The suggested architecture is adaptable to the needs of the neural network. The suggested architecture is flexible enough to compute various networks with any size input image. System-on-Chip (SoC) development for this accelerator is required in order to provide a single-chip solution for CNN edge inference that incorporates preprocessing of the data and layerwise control over the inputs and outputs.  The tight coupling between the accelerator and other components, such as memory interfaces and peripherals, is made possible by integrating our accelerator, also known as the NPU (Neural Processing Unit), into a SoC. This makes data movement more effective and lowers the latency, which enhances the overall performance of the deep learning tasks. Also the host processing system, such as a CPU or GPU, can be seamlessly integrated with the NPU as a result of a SoC.

Through this connection, it is possible to transfer computationally demanding Deep Learning (DL) activities to the accelerator, offloading the host CPU for other tasks and enhancing system efficiency. The advantage of future extension and upgrades is available with a SoC-based NPU. The SoC can be upgraded to include superior accelerator architectures or other hardware components to keep up with the developments in DL capabilities as algorithms and technology evolve. By using customized algorithms on CPU to fragment the image and a dedicated controller to keep track of the different scheduling tasks to be implemented on the NPU, this SoC will be able to handle a range of image sizes as inputs to the CNN, proving that it is an image-scalable and reconfigurable NPU-based SoC. Additionally, the SoC may increase its access to cloud resources with the aid of an Ethernet connector, allowing it to apply effective neural network preprocessing methods like network adaptation, post-training quantization and pruning for CNNs with billions of weights in a digital-twin setup.