A Variation Aware High Level Synthesis Framework

The worst-case delay/power of function units has been used in traditional high level synthesis to facilitate design space exploration. As technology scales to nanometer regime, the impact of process variations increases. The degree of variability encountered in the new process technologies makes worst-case analysis undesirable, because it may result in unexpected performance/power discrepancy or a pessimistic estimation, and may end up using excess resources to guarantee design constraints. In this paper, we propose a high level synthesis framework to take into account of the performance/power variation for function units. An effective metric called parametric yield, which is defined as the probability of the synthesized data flow graph (DFG) meeting the performance and power constraints, is used to guide scheduling, module selection, and resource sharing. An efficient performance/power yield perturbation computation method for DFG significantly improves the effectiveness of our yield driven high level synthesis algorithm. The experimental results show that our variation-aware synthesis framework achieves significant yield improvements, and has much faster (3X) runtime speed compared against previous approach.