Dynamic management of multikernel multithread accelerators using Dynamic Partial Reconfiguration

Ever demanding systems with restricted resources face increasingly complex applications. Additionally, changeable environments modify working conditions over time. Therefore, a dynamic resource management is required in order to provide adaptation capabilities. By using ARTICo
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, a bus-based architecture with reconfigurable slots, this adaptation is accomplished in three different but dependent areas: Consumption, Confidentiality and fault tolerance, and Computation. The proposed resource management strategies rely on an architecture and a model of computation that make execution configuration to be application-independent, but context-aware, since a CUDA-like execution model is used. The inherent and explicit application-level parallelism of multithreaded CUDA kernels is used to generate hardware accelerators that act as thread blocks. Despite other modes of operation provided by the ARTICo
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architecture, like module redundancy or dual-rail operation to mitigate Side-Channel Attacks, these thread blocks are dynamically managed and their execution is scheduled using a multiobjective optimization algorithm.