FPGA implementation for explainable machine learning and deep learning models to real-time problems

Machine learning models are an effective means to develop intelligent solutions for real-world problems where a high level of computation is involved. The accuracy and effectiveness of the ML/DL model are a major concern of the designer and future model validity after deploying in real-time situations. Convolutional neural network (CNN) models depend on fewer parameters as compared to any other DL or ML models. Embedded processors-based SoCs were used to implement such highly accurate CNN models. FPGA is one of the means with reconfigurable computing powers that work on the principle of the logic array and help in the decision-making of several ML models. FPGAs provide an explanatory way of implementing ML/DL models that leads to a better solution. High-throughput CNN models utilize ASIC along with FPGA and GPU for solving complex learning problems. Shortest-path computation units designed with FPGAs are popular in many embedded IoT platforms. Genetic algorithm (GA) or brainstorm optimization (BSO) with metaheuristic process are frequently used to design FPGA-based high-performance hardware accelerators. FPGAs are used for many real-time systems specifically in secured IP ownership, high-quality audio, secured image, and video processing with reduced latency and power consumption. Multicore processor-based system-on-chip (SoC) FPGAs with high-level programming abstractions are also considered in image and video processing for multimedia applications.