Access pattern based local memory customization for low power embedded systems.

Memory accesses represent a major bottleneck in embedded systems power and performance. Traditionally the local memory relied on a large cache to store all the variables in the application. However especially in large real-life applications, different types of data exhibit divergent types of locality and access patterns, with diverse locality and bandwidth needs. Traditional caches had to compromise between the different types of locality required by the access patterns, and trade-off performance against bandwidth requirement. Instead, our approach customizes the local memory architecture matching the diverse access patterns and locality types present in the application, to reduce the main memory bandwidth requirement, and significantly improve power consumption, without sacrificing performance. Our approach generated an average 30% memory power reduction without degrading performance on a set of large multimedia/general purpose applications and scientific kernels, over the best traditional cache co,configuration of similar size, demonstrating the utility of our algorithm.