The invention discloses a deep learning accelerator suitable for a stacked hourglass network. A parallel computing layer computing unit improves the computing parallelism, and a data caching module improves the utilization rate of data loaded into the accelerator while accelerating the computing speed; and meanwhile, a data adjuster in the accelerator can carry out self-adaptive data arrangement sequence change according to different calculation layer operations, so that the integrity of acquired data can be improved, the data acquisition efficiency is improved, and the time delay of a memoryaccess process is reduced. Therefore, according to the accelerator, the memory bandwidth is effectively reduced by reducing the number of memory accesses and improving the memory access efficiency while the algorithm calculation speed is increased, so that the overall calculation acceleration performance of the accelerator is realized.