Weighted Round Robin Configuration for Worst-Case Delay Optimization in Network-on-Chip

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We propose an approach for computing the end-to-end delay bound of individual variable bit-rate flows in an First Input First Output multiplexer with aggregate scheduling under weighted round robin (WRR) policy. To this end, we use a network calculus to derive per-flow end-to-end equivalent service curves employed for computing least upper delay bounds (LUDBs) of the individual flows. Since the real-time applications are going to meet guaranteed services with lower delay bounds, we optimize the weights in WRR policy to minimize the LUDBs while satisfying the performance constraints. We formulate two constrained delay optimization problems, namely, minimize-delay and multiobjective optimization. Multiobjective optimization has both the total delay bounds and their variance as the minimization objectives. The proposed optimizations are solved using a genetic algorithm. A video object plane decoder case study exhibits a 15.4% reduction of the total worst case delays and a 40.3% reduction on the variance of delays when compared with round robin policy. The optimization algorithm has low run-time complexity, enabling quick exploration of the large design spaces. We conclude that an appropriate weight allocation can be a valuable instrument for the delay optimization in on-chip network designs.