BEHAVIORAL OPTIMIZATION USING THE MANIPULATION OF TIMING CONSTRAINTS

We introduce a transformation, named rephasing, that manipulates the t iming parameters in control-data-flow graphs (CDFG's) during the high- level synthesis of data-path-intensive applications. Timing parameters in such CDFG's include the sample period, the latencies between input -output pairs, the relative times at which corresponding samples becom e available on different inputs, and the relative times at which the c orresponding samples become available at the delay nodes.; While some of the timing parameters may be constrained by performance requirement s, or by the interface to the external world, others remain free to be chosen during the process of high-level synthesis. Traditionally high -level synthesis systems for data-path-intensive applications either h ave assumed that all the relative times, called phases, when correspon ding samples are available at input and delay nodes are zero (i.e., al l input and delay node samples enter at the initial cycle of the sched ule) or have automatically assigned values to these phases as part of the data-path allocation/scheduling step in the case of newer schedule rs that use techniques like overlapped scheduling to generate complex time shapes. Rephasing, however, manipulates the values of these phase s as an algorithm transformation before the scheduling/allocation stag e, The advantage of this approach is that phase values can be chosen t o transform and optimize the algorithm for explicit metrics such as ar ea, throughput, latency, and power, Moreover, the rephasing transforma tion can be combined with other transformations such as algebraic tran sformations. We have developed techniques for using rephasing to optim ize a variety of design metrics, and our results show significant impr ovements in several design metrics. We have also investigated the rela tionship and interaction of rephasing with other high-level synthesis tasks.