MultiObjective Optimization of Channel Mapping for FailOperational Hybrid TDM NoCs Despite their technical and economic advantages, multicore processors are hesitantly adopted in safetycritical embedded application domains. A key issue is to provide hard realtime guarantees and faulttolerance for critical applications as well as isolation between applications of different criticality on shared resources such as the interconnect. To address this, hybrid NoCs that combine configurable TimeDivisionMultiplexing (TDM) for critical traffic with packet switching for BestEffort (BE) traffic have been proposed. More recently, hybrid NoCs with protection switching schemes have been introduced to cope with failoperational applications. Although they can handle failures happening in a NoC and guarantee realtime requirements by design, the problem of providing optimized mapping of critical tasks while considering the properties of protection switching schemes still remains. In this paper, we propose a multiobjective optimization methodology to tackle this matter. We first propose a model to map TDM channels onto a given NoC topology while considering protection switching schemes. The implemented objectives are defined so as to evaluate the behavior of the TDM mapping while still capturing its impact on the BE traffic, since a complete simulation considering BE would be time consuming (hundreds of thousands of NoC cycles with different injection rates). We then show how beneficial simultaneously optimizing several objectives is when using hybrid NoCs. The proposed methodology can indeed enable design space exploration in order to find feasible mappings for application specific scenarios and broad tradeoff analyzes without having to run prohibitively long BE traffic simulations.

LearningBased Design Space Exploration of Emerging 3D NoC Architectures In the absence of Moorexe2x80x99s Law, threedimensiona a promising direction to satisfy the demand from bigdata applications. However, due to the growing heterogeneity, system size, and issues stemming from emerging interconnecl (3D) manycore systems that incorporate both CPUs and GPUs presentsts such as monolithic 3D (M3D), it is difficult to satisfactorily explore the design space in reasonable amounts of time. In this paper, we reinforce the need for multiobjective formulations (optimizing for power, performance, thermals, etc. simultaneously) and present a solution that combines machine learning and local searches to quickly and intelligently explore the design space. In addition, we look at two specific problems (TSVbased 3D heterogeneous manycore systems and M3Dbased NoC) and what considerations need to be made at designtime.