A fast and efficient algorithm for finding empty area is necessary for online placement, task relocation and defragmentation on a partially reconfigurable FPGA. We present an algorithm that finds empty area as a list of overlapping maximal rectangles. Using an innovative representation of the FPGA, we are able to predict possible locations of the maximal empty rectangles. Worst-case time complexity of our algorithm is O(xy) where x is the number of columns, y is the number of rows and x.y is the total number of cells on the FPGA. Experiments show that, in practice, our algorithm needs to scan less than 15% of the FPGA cells to make a list of all maximal empty rectangles.

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Transformative applications are computation intensive applications characterized by iterative dataflow behavior. Typical examples are image processing applications like JPEG, MPEG, etc. The performance of embedded hardware-software systems that implement transformative applications can be maximized by obtaining a pipelined design. We present a tool for hardware-software partitioning and pipelined scheduling of transformative applications. The tool uses iterative partitioning and pipelined scheduling to obtain optimal partitions that satisfy the timing and area constraints. The partitioner uses a branch and bound approach with a unique objective function that minimizes the initiation interval of the final design. We present techniques for generation of good initial solution and search-space limitation for the branch and bound algorithm. A candidate partition is evaluated by generating its pipelined schedule. The scheduler uses a novel retiming heuristic that optimizes the initiation interval, number of pipeline stages, and memory requirements of the particular design alternative. We evaluate the performance of the retiming heuristic by comparing it with an existing technique. The effectiveness of the entire tool is demonstrated by a case study of the JPEG image compression algorithm. We also evaluate the run time and design quality of the tool by experimentation with synthetic graphs.

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This paper presents an integrated approach to hardware software p artitioning and hardware design space exploration. We propose a genetic algorithm which performs hardware software p artitioning on a task graph while simultaneously contemplating various design alternatives for tasks mapped to hardware. We primarily deal with data dominated designs typically found in digital signal processing and image processing applications. A detailed description of various genetic operators is presented. We provide results to illustrate the e ectiveness of our integrated methodology.

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