Dynamic management of multikernel multithread accelerators using dynamic partial reconfiguration

Abstract

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 ARTICo3, 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 ARTICo3 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.