Improvements in computer performance depend increasingly on specialized accelerators and recently, numerous architectures optimized for sparse matrix kernels have been proposed, however, they do not exploit the structural properties of the matrices. SpDCache is a cache for outer-product Sparse Matrix-Vector Multiplication (SpMV) which has storage strategies optimized for both dense and sparse regions and which performs reductions locally in this cache. Real world matrices typically have a dense band which benefits from being blocked in the dense region of our cache, while the sparse regions benefit from fine-grained storage and a shift of the computation close to the main memory. We present the architectural principals of SpDCache and show that it reduces main memory traffic by ~8x and increases the cache utilization by ~2x for banded matrices.