The VolumePro Real-Time Ray-Casting System

This paper describes VolumePro, the world’s first single-chip realtime volume rendering system for consumer PCs. VolumePro implements ray-casting with parallel slice-by-slice processing. Our discussion of the architecture focuses mainly on the rendering pipeline and the memory organization. VolumePro has hardware for gradient estimation, classification, and per-sample Phong illumination. The system does not perform any pre-processing and makes parameter adjustments and changes to the volume data immediately visible. We describe several advanced features of VolumePro, such as gradient magnitude modulation of opacity and illumination, supersampling, cropping and cut planes. The system renders 500 million interpolated, Phong illuminated, composited samples per second. This is sufficient to render volumes with up to 16 million voxels (e.g., 2563) at 30 frames per second.Engineering and Applied Science

Extracting Data-Level Parallelism in High-Level Synthesis for Reconfigurable Architectures

High-Level Synthesis (HLS) tools are a set of algorithms that allow programmers to obtain implementable Hardware Description Language (HDL) code from specifications written high-level, sequential languages such as C, C++, or Java. HLS has allowed programmers to code in their preferred language while still obtaining all the benefits hardware acceleration has to offer without them needing to be intimately familiar with the hardware platform of the accelerator. In this work we summarize and expand upon several of our approaches to improve the automatic memory banking capabilities of HLS tools targeting reconfigurable architectures, namely Field-Programmable Gate Arrays or FPGA\u27s. We explored several approaches to automatically find the optimal partition factor and a usable banking scheme for stencil kernels including a tessellation based approach using multiple families of hyperplanes to do the partitioning which was able to find a better banking factor than current state-of-the-art methods and a graph theory methodology that allowed us to mathematically prove the optimality of our banking solutions. For non-stencil kernels we relaxed some of the conditions in our graph-based model to propose a best-effort solution to arbitrarily reduce memory access conflicts (simultaneous accesses to the same memory bank). We also proposed a non-linear transformation using prime factorization to convert a small subset of non-stencil kernels into stencil memory accesses, allowing us to use all previous work in memory partition to them. Our approaches were able to obtain better results than commercial tools and state-of-the-art algorithms in terms of reduced resource utilization and increased frequency of operation. We were also able to obtain better partition factors for some stencil kernels and usable baking schemes for non-stencil kernels with better performance than any applicable existing algorithm

Automatic License Plate Recognition Using Deep Learning Techniques

Automatic License Plate Recognition (ALPR) systems capture a vehicles license plate and recognize the license number and other required information from the captured image. ALPR systems have number of significant applications: law enforcement, public safety agencies, toll gate systems, etc. The goal of these systems is to recognize the characters and state on the license plate with high accuracy. ALPR has been implemented using various techniques. Traditional recognition methods use handcrafted features for obtaining features from the image. Unlike conventional methods, deep learning techniques automatically select features and are one of the game changing technologies in the field of computer vision, automatic recognition tasks, natural language processing. Some of the most successful deep learning methods involve Convolutional Neural Networks. This research applies deep learning techniques to the ALPR problem of recognizing the state and license number from the USA license plate. Existing ALPR systems include three stages of processing: license plate localization, character segmentation and character recognition but do little for the state recognition problem. Our research not only extracts the license number, but also processes state information from the license plate. We also propose various techniques for further research in the field of ALPR using deep learning techniques