Self-adaptive harris corner detector on heterogeneous many-core processor

The recent years have shown the emergence of heterogeneous system architecture (HSA), which offers massive computational power assembled into a compact design. Computer vision applications with massive inherent parallelism highly benefits from such heterogeneous processors with on-chip CPU and GPU units. The highly parallel and compute intensive parts of the application program can be mapped to the GPU while the control flow and high level tasks may run on the CPU. However, they pose considerable challenge to software development due to their hybrid architecture. Sharing of resources (GPU or CPU) among applications running concurrently, leads to variations in processing interval and prolonged processing intervals leads to low quality results (frame drops) for computer vision algorithms. In this work, we propose resource-awareness and self organisation within the application layer to adapt to available resources on the heterogeneous processor. The benefits of the new model is demonstrated using a widely used computer vision algorithm called Harris corner detector. A resource-aware runtime-system and a heterogeneous processor were used for evaluation and the results indicate a well constrained processing interval and reduced frame-drops. Our evaluations demonstrate up to 20% improvements in processing rate and accuracy of the detected corner points for Harris corner detection.