Efficient architectures through application clustering and architectural heterogeneity

Customizing architectures for particular applications is a promising approach to yield highly energy-efficient designs for embedded systems. This work explores the benefits of architectural customization for a class of embedded architectures typically used in energy-constrained application domains such as sensor node and multimedia processing. We implement a process flow that analyzes runtime profiles of applications and combines this information with a model for our architectural design space providing a robust customization engine built upon a fully automated method for determining an efficient architecture (together with appropriate application transformations). By profiling embedded benchmarks from a variety of sensor and multimedia applications, the paper shows the relative energy savings resulting from various architectural optimizations and identifies the number of architectures that achieves near-optimal savings for a group of applications. This paper proposes the use of heterogeneous chip-multiprocessors as a cost-effective approach to capitalize on the potential energy savings provided by application customization while executing a range of applications efficiently.