Hardware Acceleration For Deep Learning Model

In an era defined by fast technological growth, deep learning has sparked a revolution in artificial intelligence. With this incredible breakthrough, the possibility to unlock previously unimaginable possibilities lies within the depths of innovative hardware acceleration approaches. Deep learning models are computationally intensive and require a significant amount of processing power. FPGAs have emerged as a popular choice for accelerating the training and inference of deep learning models due to their highly parallel architecture, customization, and flexibility, while achieving high parallelism, low power consumption, and better programmability. In this study, we propose an approach that combines FPGA-based hardware acceleration with software-based optimization techniques, using the latest design tools to achieve high performance and efficiency in deep learning applications. Through trials on a facial recognition dataset, we show that our technique outperforms existing software-based approaches in terms of speed and accuracy. Our methodology includes Int-8 bit quantization as a critical step in preparing the trained facial recognition network for hardware deployment. This quantization reduces the numerical precision of the weights and activations in a deep learning model to 8 bits, significantly reducing memory usage and computational complexity while maintaining acceptable accuracy. Our approach showed a significant speedup and better utilization of hardware resources, obtaining a 3. 52x increase in FPS, a 2. 83x decrease in latency, a 1. 5x reduction in LUT utilization, and a 1. 25x reduction in BRAM utilization. In conclusion, this study tackles the challenges of enhancing efficiency and performance in deep learning models, with a particular focus on the application of face recognition.