Optical flow (OF) is widely used for motion detection. Existing hardware accelerators for OF typically prioritize performance, often overlooking the strict power constraints of edge devices such as mobile robots and drones. This paper explores energy-efficient processing elements for implementing the Lucas-Kanade OF algorithm, focusing on parallelism and resource sharing. Finally, we evaluate the energy consumption of these processing elements through FPGA-based design.