Existing cache organization suffers from the inability to distinguish different types of localities, and non-selectively cache all data rather than making any attempt to take special advantage of the locality type. This causes unnecessary movement of data among the levels of the memory hierarchy and increases in miss ratio. In this dissertation I propose a split data cache architecture that will group memory accesses as scalar or array references according to their inherent locality and will subsequently map each group to a dedicated cache partition. In this system, because scalar and array references will no longer negatively affect each other, cache-interference is diminished, delivering better performance. Further improvement is achieved by the introduction of victim cache, prefetching, data flattening and reconfigurability to tune the array and scalar caches for specific application. The most significant contribution of my work is the introduction of novel cache architecture for embedded microprocessor platforms. My proposed cache architecture uses reconfigurability coupled with split data caches to reduce area and power consumed by cache memories while retaining performance gains. My results show excellent reductions in both memory size and memory access times, translating into reduced power consumption. Since there was a huge reduction in miss rates at L-1 caches, further power reduction is achieved by partially or completely shutting down L-2 data or L-2 instruction caches. The saving in cache sizes resulting from these designs can be used for other processor activities including instruction and data prefetching, branch-prediction buffers. The potential benefits of such techniques for embedded applications have been evaluated in my work. I also explore how my cache organization performs for non-numeric data structures. I propose a novel idea called "Data flattening" which is a profile based memory allocation technique to compress sparsely scattered pointer data into regular contiguous memory locations and explore the potentials of my proposed Spit cache organization for data treated with data flattening method
