Functional VLSI Module-based Multiprocessor Architectures for Real-time Vision

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This project focuses on system integration of VLSI functional modules by creating uniformity in their interface behavior. Such integration is aimed at designing flexible, real-time, low-level vision systems using pipelining of modules that correspond to individual operations. Two tasks are addressed in this research. First, functional modules for benchmarking vision operations are designed and characterized. Emphasis is placed on maintaining invariance in control and input/output requirements with respect to changes in the parameters of each operation. Second, algorithms and associated software for static mapping of a given sequence of operations on a set of functional modules connected through a network of switching nodes are developed. These algorithms balance the workload of the modules in order to achieve high throughput. The characterization of individual VLSI modules in terms of operation-specific input/output, buffer sizes, and memory size allows more realistic prediction of system performance. Multiprocessor architectures for vision can be classified as homogeneous architectures and heterogeneous architectures. In homogeneous architectures, exploiting parallelism optimally for the diversified operations in vision is very difficult, while in heterogeneous architectures, only a small set of low-level operations, relevant to the particular vision applications at hand, are implemented. This research addresses the important problem of developing functional modules and their integration in order to dynamically tailor vision operations to the computer architecture at run time. Success in this research allows better exploitation of parallelism for both low-level and high-level vision operations.