Design Space Exploration and Optimization of Embedded Memory Systems

Abstract

Recent years have witnessed the emergence of microprocessors that are embedded within a plethora of devices used in everyday life. Embedded architectures are customized through a meticulous and time consuming design process to satisfy stringent constraints with respect to performance, area, power, and cost. In embedded systems, the cost of the memory hierarchy limits its ability to play as central a role. This is due to stringent constraints that fundamentally limit the physical size and complexity of the memory system. Ultimately, application developers and system engineers are charged with the heavy burden of reducing the memory requirements of an application.

This thesis offers the intriguing possibility that compilers can play a significant role in the automatic design space exploration and optimization of embedded memory systems. This insight is founded upon a new analytical model and novel compiler optimizations that are specifically designed to increase the synergy between the processor and the memory system. The analytical models serve to characterize intrinsic program properties, quantify the impact of compiler optimizations on the memory systems, and provide deep insight into the trade-offs that affect memory system design.