Component Allocation Optimization for Heterogeneous CPU-GPU Embedded Systems

In a quest to improve system performance, embedded systems are today increasingly relying on heterogeneous platforms that combine different types of processing units such as CPUs, GPUs and FPGAs. However, having better hardware capability alone does not guarantee higher performance, how functionality is allocated onto the appropriate processing units strongly impacts the system performance as well. Yet, with this increase in hardware complexity, finding suitable allocation schemes is becoming a challenge as many new constraints and requirements must now be taken into consideration. In this paper, we present a formal model for allocation optimization of embedded systems which contains a mix of CPU and GPU processing nodes. The allocation takes into consideration the software and hardware architectures, the system requirements and criteria upon which the allocation should be optimized. In its current version, optimized allocation schemes are generated through an integer programming technique to balance the system resource utilization and to optimize the system performance using the GPU resources.