Power-performance trade-offs for reconfigurable computing

We explore the system-level power-performance trade-offs available when implementing streaming embedded applications on fine-grained reconfigurable architectures. We show that an efficient hardware-software partitioning algorithm is required when targeting low-power. However, if the application objective is performance, then we propose the use of dynamically reconfigurable architectures. This work presents a configuration-aware data size partitioning approach. We propose a design methodology that adapts the architecture and used algorithms to the application requirements. The methodology has been proven to work on a real research platform based on Xilinx devices. Finally, we have applied our methodology and algorithms to the case study of image sharpening, which is required nowadays in digital cameras and mobile phones.