CPU-GPU heterogeneous implementations of depth-based foreground detection

Runtime specialization for heterogeneous CPU-GPU platforms

Cache optimization for CPU - GPU heterogeneous processors ∗

Microprocessors combining CPU and GPU cores using a common last-level cache pose new challenges to cache management algorithms. Since GPU cores feature much higher data access rates than CPU cores, the majority of the available cache space will be used by GPU applications, leaving only very limited cache capacity for CPU applications, which may be disadvantageous for overall system performance....

CPU , GPU and FPGA Implementations of MALD : Ceramic Tile Surface Defects Detection Algorithm

This paper addresses adjustments, implementation and performance comparison of the Moving Average with Local Difference (MALD) method for ceramic tile surface defects detection. Ceramic tile production process is completely autonomous, except the final stage where human eye is required for defects detection. Recent computational platform development and advances in machine vision provides us wi...

GPU and CPU Cooperative Accelerated Road Detection

In this paper, we propose a fast and robust unstructured road detection method that integrates GPU (Graphics Processing Unit) and CPU implementations. In order to ensure the robustness of the algorithm, BP (Back Propagation) Neural Network is employed to learn the color features from a set of sample of both road region and off-road region, and then to classify a newly pixel. And the B-spline cu...

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Convolutional Neural Networks (CNNs) have shown to be powerful classification tools in tasks that range from check reading to medical diagnosis, reaching close to human perception, and in some cases surpassing it. However, the problems to solve are becoming larger and more complex, which translates to larger CNNs, leading to longer training times—the computational complex part—that not even the...