Distributed control synthesis for data-dependent iterative algorithms

Data-dependent control flow changes are typically implemented in complex general-purpose controllers. However, in medium to fine-grained iterative algorithms found in DSP and arithmetic, it is desirable for both cost and performance reasons to develop simplified and distributed control structures throughout the array architectures. We present a transformation technique to systematically convert an iterative algorithm whose loop bounds are data-dependent to an equivalent data-independent regular algorithm. We use the worst-case situation to define the structure of a given computation. Then, depending upon applications, various optimizations are performed to minimize cost overhead associated with data-dependent characteristics. To demonstrate the feasibility of our methodologies, we have designed and simulated parallel algorithms for two important applications using the proposed transformation. Based on the resulting algorithms, we have developed VLSI array architectures using conventional methods of regular array synthesis (S.Y. Kung, 1988). Compared with previously reported structures, we have achieved substantial improvements in terms of both performance and array costs