Digital signal processing algorithms with multiple shift-invariant dependence graphs (DGs) can be mapped to field programmable gate array hardware in many different types of systolic processor arrays. Because of the finite amount of hardware resources, the problem is to use a "right" amount of hardware in a specific configuration so to maximize the processing speed. In this paper, the problem of finding the right processor array configuration is formulated as a constrained optimization problem where the cost function includes not only the cost of individual processor arrays but also the cost of interfacing circuits. Three heuristic algorithms are presented for the optimization problem. Among them, both the Lth axial neighbor algorithm and the simulated annealing algorithm produce good results on a test case. Simulation results on the test case also indicate that the initial configuration is important in getting a good configuration for both algorithms. The Lth axial neighbor algorithm has the extra advantage of requiring less amount of performance tuning.

Mapping DSP algorithms to FPGA/VLSI circuits is an important issue in Application-Specific Array Processor design. Since a DSP algorithm can be abstracted as a graph where each node is a shift-invariant DG (Dependence Graph) and the edges denote the data flow, it is possible to map a DSP algorithm to a set of processor arrays with some interface circuits. The interface design depends on the projection/scheduling vectors used on the two corresponding shift-invariant DGs and the interfacing cost is very significant when a lot of delays are necessary or when the processor operations are relatively inexpensive in terms of area. Therefore, when selecting these vectors in a design environment, the effect on the interface circuit must be accurately computed. In this paper, various interface circuit designs are presented and categorized based on the data conversion requirement. An algorithm to select a design from many design options to minimize the cost is also described.