In designing a field-programmable gate array (FPGA)-based processor for motion stereo, a parallel memory system and a simple interconnection network for parallel data transfer are essential for parallel image processing. This paper, firstly, presents an FPGA-oriented hierarchical memory system. To reduce the bandwidth requirement between an on-chip memory in an FPGA and external memories, we propose an efficient scheduling: Once pixels are transferred to the on-chip memory, operations associated with the data are consecutively performed. Secondly, a rectangular memory allocation is proposed which allocates pixels to be accessed in parallel onto different memory modules of the on-chip memory. Consequently, completely parallel access can be achieved. The memory allocation also minimizes the required capacity of the on-chip memory and thus is suitable for FPGA-based implementation. Finally, a functional unit allocation is proposed to minimize the complexity between memory modules and functional units. An experimental result shows that the performance of the processor becomes 96 times higher than that of a 400 MHz Pentium II.

Three-dimensional (3-D) instrumentation using an image sequence is a promising instrumentation method for intelligent systems in which accurate 3-D information is required. However, real-time instrumentation is difficult since much computation time and a large memory bandwidth are required. In this paper, a 3-D instrumentation VLSI processor with a concurrent memory-access scheme is proposed. To reduce the access time, frequently used data are stored in a cache register array and are concurrently transferred to processing elements using simple interconnections to the 8-nearest neighbor registers. Based on a row and column memory access pattern, we propose a diagonally interleaved frame memory by which pixel values of a row and column are stored across memory modules. Based on the concurrent memory-access scheme, a 40 GOPS vprocessor is designed and the delay time for the instrumentation is estimated to be 42 ms for a 256256 images.

In a real-time vision system, parallel memory access is essential for highly parallel image processing. The use of multiple memory modules is one efficient technique for parallel access. In the technique, data stored in different memory modules can be accessed in parallel. This paper presents an optimal memory allocation methodology to map data to be read in parallel onto different memory modules. Based on the methodology, a high-performance VLSI processor for three-dimensional instrumentation is proposed.