Design and Analysis of an Enhanced Cnn Accelerator for Deep Learning Applications

This paper presents an optimized Convolutional Neural Network (CNN) accelerator with a focus on improving power efficiency and computational performance. Traditional CNN accelerators often suffer from high power consumption and increased latency due to redundant switching activities. To address these challenges, we implement clock gating as a power optimization technique to minimize dynamic power usage while maintaining computational accuracy. The proposed design achieves significant improvements in resource utilization, reducing the number of LUTs from 300 to 150. Power analysis indicates a drastic reduction in power consumption from 1.674 W to 0.133 W, while delay is also improved from 4.861 ns to 3.521 ns. The power-delay product (PDP) is reduced from 8.137 to 0.468, highlighting the effectiveness of clock gating in CNN acceleration. The design was synthesized and evaluated using FPGA-based implementation, demonstrating substantial gains in energy efficiency. These findings suggest that integrating clock gating into CNN accelerators can lead to substantial power savings while maintaining high computational efficiency, making it an ideal solution for energy-constrained edge and embedded AI applications.