With energy-efficient computation, processing-inmemory (PIM) architectures have been highlighted as one of the most viable candidates to substitute the traditional ones. Recently, shift and duplicate kernel (SDK) mapping method was proposed to enable efficient and fast convolutional neural networks (CNNs) inference in the PIM array. However, since its weight deployment to reuse input data, this method generates idle cells that do not involved in the computation, which leads to an increase of energy consumption. In this paper, we propose a novel weight mapping method called kernel-grouping aided row-skipping (KARS). KARS maximizes utilization by removing idle cells on a PIM array and reduces computing cycles. In comparison to the traditional methods, KARS achieves a speedup by up to 3× at Layer 2 of VGGNet-13 and ResNet-18.