We propose a sparse matrix rearrangement algorithm with a novel 3D-SRAM-centric Polyomino architecture which makes it possible to efficiently process the rearranged matrix. By rearranging randomly pruned, irregularly structured sparse matrices into regularly structured matrices, the compression ratio of the data increases. The rearrangement algorithm can be implemented simply by attributing it to the widely known k-sum problem. We also propose a compression format for storing the rearranged matrices, which can reduce the amount of required memory by 63% compared with the conventional method. The proposed Polyomino architecture can efficiently process rearranged matrices by using a 3D stacked SRAM.