Parallel Architectures and Parallel Algorithms for Integrated Vision Systems

Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g., object recognition). An IVS normally involves algorithms from low level, intermediate level, and high level vision. Designing parallel architectures for vision systems is of tremendous interest to researchers. Several issues are addressed in parallel architectures and parallel algorithms for integrated vision systems

Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems

Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ). This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)

GPU accelerated parallel Iris segmentation

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the person. Iris recognition systems are the most definitive biometric system since complex random iris patterns are unique to each individual and do not change with time. Iris Recognition is basically divided into three steps, namely, Iris Segmentation or Localization, Feature Extraction and Template Matching. To get a performance gain for the entire system it becomes vital to improve performance of each individual process. Localization of the iris borders in an eye image can be considered as a vital step in the iris recognition process due to high processing required. The Iris Segmentation algorithms are currently implemented on general purpose sequential processing systems, such as common Central Processing Units (CPUs). In this thesis, an attempt has been made to present a more straight and parallel processing alternative using the graphics processing unit (GPU), which originally was used exclusively for visualization purposes, and has evolved into an extremely powerful coprocessor, offering an opportunity to increase speed and potentially intensify the resulting system performance. To realize a speedup in Iris Segmentation, NVIDIA’s Compute Unified Device Architecture (CUDA) programming model has been used. Iris Localization is achieved by implementing Hough Circular Transform on edge image obtained by using Canny edge detection technique. Parallelism is employed in Hough Transformation step

Evaluation of High Performance Fortran through Application Kernels

Since the definition of the High Performance Fortran (HPF) standard, we have been maintaining a suite of application kernel codes with the aim of using them to evaluate the available compilers. This paper presents the results and conclusions from this study, for sixteen codes, on compilers from IBM, DEC, and the Portland Group Inc. (PGI), and on three machines: a DEC Alphafarm, an IBM SP-2, and a Cray T3D. From this, we hope to show the prospective HPF user that scalable performance is possible with modest effort, yet also where the current weaknesses lay