Power aware heterogeneous MPSoC with dynamic task scheduling and increased data locality for multiple applications

Abstract

A new heterogeneous multiprocessor system with dynamic memory and power management for improved performance and power consumption is presented. Increased data locality is automatically revealed leading to enhanced memory access capabilities. Several applications can run in parallel sharing processing elements, memories as well as the interconnection network. Real time constraints are regarded by prioritization of processing element allocation, scheduling and data transfers. Scheduling and allocation is done dynamically according to runtime data dependency checking. We are able to show that execution times, bandwidth demands and power consumption are decreased. A tool flow is introduced for an easy generation of the hardware platform and software binaries for cycle accurate simulations. Further newly developed tools are available for power analysis, data transfer observation and task execution visualization.