Dataflow-based Adaptation Framework with Coarse-Grained Reconfigurable Accelerators

Today, the demand of adaptive systems is constantly growing, especially in hard-constrained contexts such as Cyber-Physical Systems. However, the efficient management of such platforms requires dealing with several issues such as the real-time execution, energy saving and dynamic context changes. Such strict requirements imply a high flexibility of the application and of the architecture on which it is executed. Runtime managers offer the possibility to dynamically schedule and map an application on the available software processing units. However, hardware acceleration may also be necessary for computationally-intensive workloads that depend on the running functionality, additionally complicating runtime management. Coarse-Grained Reconfigurable (CGR) accelerators have the ability to switch among different domain-specific functionalities with a small overhead. To support energy and time adaptivity in heterogeneous systems, and to exploit multi-core architectures and CGR accelerators, this work proposes the combination of the SPIDER software runtime manager and the dataflow-to-hardware MDC design suite for CGR accelerators.