The combination of diffusion strategies and least-mean-square (LMS) algorithm provides many advantages for adaptive-filter to solve distributed optimization, estimation and inference problems. However, suffering from high computation complexity, software implementation of diffusion LMS algorithm is unsuitable for real-time and portable applications. In order to extend its availability, we design a reconfigurable parallel FPG accelerator by exploring multiple dimensions of parallelism, including: parallel execution of agents state updating, data combining, data training and multi-stages pipeline to speedup the execution time. The accelerator for networks with various number of agents and different input dimensions is implemented. Results demonstrate that, it can achieve a speedup of three orders of magnitude at 100Mhz compared with C implementation for a 32-nodes network with 16-dimensional input-data.