Profile assisted online system-level performance and power estimation for dynamic reconfigurable embedded systems

Significant research has demonstrated the performance and power benefits of runtime dynamic reconfiguration of FPGAs and microprocessor/FPGA devices. For dynamically reconfigurable systems, in which the selection of hardware coprocessors to implement within the FPGA is determined at runtime, online estimation methods are needed to evaluate the performance and power consumption impact of the hardware coprocessor selection. In this paper, we present a profile assisted online system-level performance and power estimation framework for estimating the speedup and power consumption of dynamically reconfigurable embedded systems. We evaluate the accuracy and fidelity of our online estimation framework for dynamic hardware kernel selection to maximize performance or minimize system power consumption.