Exploring Power and Throughput for Dataflow Applications on Predictable NoC Multiprocessors

System level optimization for multiple mixed-criticality applications on shared networked multiprocessor platforms is extremely challenging. Substantial complexity arises from the interdependence between the multiple subproblems of mapping, scheduling and platform configuration under the consideration of several, potentially orthogonal, performance metrics and constraints. Instead of using heuristic algorithms and problem decomposition, novel unified design space exploration (DSE) approaches based on Constraint Programming (CP) have in the recent years shown promising results. The work in this paper takes advantage of the modularity of CP models, in order to support heterogeneous multiprocessor Network-on-Chip (NoC) with Temporally Disjoint Networks (TDNs) aware message injection. The DSE supports a range of design criteria, in particular the optimization and satisfaction of power and throughput. In addition, the DSE now provides a valid configuration for the TDNs that guarantees the performance required to fulfil the design goals. The experiments show the capability of the approach to find low-power and high-throughput designs, and validate a resulting design on a physical TDN-based NoC implementation.QC 20181114