The toughness and complexity of the computational problems which human beings tackle rise faster than the computational platforms themselves. Moreover, the dark silicon era negatively effects the traditional computational platforms and contributes unfavorably to this gap. These situations require the alternative computing paradigms, ranging from multi-core CPUs to GPUs and even untraditional paradigms such as in-memory computing. Associative processing (AP) is a promising candidate for in-memory computing where the computation is performed on the memory rows without moving the data. Even though APs propose a good solution for the memory bottleneck, their power density poses an issue because of the huge switching activity on the rows happens during the operations. In this study, we seek a low-power AP implementation by proposing architectural and instructional improvements to decrease the switching activity. The simulations on various benchmarks from different domains show that the proposed low-power AP methods provide energy reduction up to 48% with a negligible impact on the area and performance.