Konstantin Shkurko

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We propose two hardware mechanisms to decrease energy consumption on massively parallel graphics processors for ray tracing while keeping performance high. First, we use a streaming data model and configure part of the L2 cache into a ray stream memory to enable efficient data processing through ray reordering. This increases the L1 hit rate and reduces(More)
We propose two hardware mechanisms to decrease energy consumption on massively parallel graphics processors for ray tracing. First, we use a streaming data model and configure part of the L2 cache into a ray stream memory to enable efficient data processing through ray reordering. This increases L1 hit rates and reduces off-chip memory energy substantially(More)
This paper introduces a composite learning approach for image retrieval with relevance feedback. The proposed system combines the radial basis function (RBF) based low-level learning and the semantic learning space (SLS) based high-level learning to retrieve the desired images with fewer than 3 feedback steps. User's relevance feedback is utilized for(More)
This paper introduces a short-term and long-term learning approach for Content-Based Image Retrieval with relevance feedback. The proposed system combines Radial Basis Function (RBF) network and the Semantic Space methods. The RBF Subsystem captures the non-linear relationship between the low-level features and the semantic meaning within an image, while(More)
Hardware acceleration for ray tracing has been a topic of great interest in computer graphics. However, even with proposed custom hardware, the inherent irregularity in the memory access pattern of ray tracing has limited its performance, compared with rasterization on commercial GPUs. We provide a different approach to hardware-accelerated ray tracing,(More)
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