Hardware implementation of convolutional neural networks (CNNs) using field-programmable gate arrays (FPGAS)

Because of the parallelism and reprogrammability of these digital devices, the implementation of Artificial Neural Networks (ANNs) using Field Programmable Gate Arrays (FPGAs) has garnered a significant amount of attention. This is because FPGAs provide a superior alternative to conventional VLSI designs and traditional software approaches. Convolutional Neural Networks (CNNs), despite their high accuracy in image classification, pose challenges in computational complexity, especially for real-time applications. Graphics processing units (GPUs) is standard for CNN acceleration, but their excessive power consumption makes them impractical for use in common devices. In other hand, field-programmable gate arrays (FPGAs) offer a possible solution because of their low power consumption and flexible architecture. The parallelism of layer resources and pipelining within layers make the utilization be more boost speed. Hardware and software partitioning can be accomplished through the utilization of tools such as Xilinx SDSOC, which unites CPUs and FPGAs on the same board in order to optimize design time and handle larger designs. The programmability of FPGAs enables the investigation of novel neural network techniques and the resolution of large-scale problems that are not achievable with traditional processors. Utilizing FPGAs to implement ANNs takes use of digital system designs defined using VHDL, which improves the efficiency of low-precision neural networks in real-time classification tasks. Although, there are still obstacles when it comes to implementing artificial neural networks (ANNs) with a high number of neurons using FPGA. The solutions to these issues, the outcomes of the implementations, and the possibilities for future development highlight the increasing attention and continuous endeavors in this field.