FPGA Implementation of Image Registration Using Accelerated CNN

In particular, applications involving image identification and recognition have shown that convolution neural networks, often known as CNNs, are successful in performing these tasks. However, they demand extensive CPU operations and memory bandwidth, which prevents conventional CPUs from reaching the appropriate levels of performance. To be more specific, field programmable gate arrays (FPGA) have lately been used for the purpose of speeding up the construction of deep learning networks. In this article, we take a look at some of the more current and established methods for speeding up deep learning networks using FPGAs. We focus on the fundamental components that are used by the numerous methods for enhancing the acceleration performance. We provide some suggestions for making the most of FPGAs when it comes to the acceleration of CNNs. As a result, it is anticipated that this evaluation will guide the future developments on effective hardware accelerators and will be helpful to academics working in deep learning. In this work, an FPGA-based ultrasonic IR CNN called FUIR-CNN is proposed for the purpose of regressing three stiff registration parameters from picture pairs. We verify the findings utilizing the Zynq-7100 on three different datasets and architectures of differing sizes, comparing them to those obtained using a CPU and a GPU. When it comes to training times, GPUs regularly beat FPGAs in situations involving batch processing. However, when it comes to scenarios involving data streams, FPGA designs obtain a significant speedup in comparison to GPU and CPU provided sufficient resources are committed to the learning job.