title: DROPc-Dynamic Resource Optimization for Convolution Layer
abstract: The computational complexity of convolutional neural networks (CNNs) becomes challenging for resource-constrained hardware devices. The convolution layer is predominant in the overall CNN architecture, performing the expensive multiplication and accumulation operation. Therefore, designing a hardware-efficient convolution layer will effectively improve the overall performance of a CNN. In this research, we propose a dynamic resource optimization (DROP) approach to improve the power and delay of 
journal: Electronics