Behavioral optimization using the manipulation of timing constraints

We introduce a transformation, named rephasing, that manipulates the timing 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 become available on different inputs, and the relative times at which the corresponding samples become available at the delay nodes. While some of the timing parameters may be constrained by performance requirements, 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 have assumed that all the relative times, called phases, when corresponding samples are available at input and delay nodes are zero (i.e., all input and delay node samples enter at the initial cycle of the schedule) or have automatically assigned values to these phases as part of the data-path allocation/scheduling step in the case of newer schedulers that use techniques like overlapped scheduling to generate complex time shapes. Rephasing, however, manipulates the values of these phases as an algorithm transformation before the scheduling/allocation stage. The advantage of this approach is that phase values can be chosen to transform and optimize the algorithm for explicit metrics such as area, throughput, latency, and power. Moreover, the rephasing transformation can be combined with other transformations such as algebraic transformations. We have developed techniques for using rephasing to optimize a variety of design metrics, and our results show significant improvements in several design metrics. We have also investigated the relationship and interaction of rephasing with other high-level synthesis tasks.