Optimized Deep learning for Energy Efficient Low-powered FPGAs

This paper comprehensively evaluates the performance of Field-Programmable Gate Arrays (FPGAs) against conventional hardware such as GPUs and CPUs in deep learning tasks. The evaluation focuses on several key metrics, including efficiency, accuracy, processing time, Mean Squared Error (MSE), precision, F1 score, hardware utilization, and resource consumption. We tested both BERT for text classification and Convolutional Neural Networks for image classification, providing insights into how these hardware platforms handle diverse deep learning workloads.Our results indicate that FPGAs, while more challenging to implement, offer significant benefits in scenarios where low latency, power efficiency, and high precision are critical. Specifically, they excel in applications requiring optimized performance per watt and fine-grained control over hardware resources. However, GPUs remain the preferred choice for general-purpose deep learning, due to their superior raw computational power, larger ecosystem, and ease of development. The findings underscore the potential of FPGAs in niche applications where energy constraints and specialized tasks take precedence, while GPUs continue to dominate in mainstream deep learning environments.