Fractional rate dataflow model and efficient code synthesis for multimedia applications
Proceedings of the joint conference on Languages, compilers and tools for embedded systems: software and compilers for embedded systems
Software Synthesis from Dataflow Graphs
Software Synthesis from Dataflow Graphs
PDP '96 Proceedings of the 4th Euromicro Workshop on Parallel and Distributed Processing (PDP '96)
Scheduling dynamic dataflow graphs with bounded memory using the token flow model
Scheduling dynamic dataflow graphs with bounded memory using the token flow model
Teleport messaging for distributed stream programs
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Buffer capacity computation for throughput-constrained modal task graphs
ACM Transactions on Embedded Computing Systems (TECS)
IEEE Transactions on Signal Processing
Parameterized dataflow modeling for DSP systems
IEEE Transactions on Signal Processing
Hierarchical finite state machines with multiple concurrency models
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
SPDF: a schedulable parametric data-flow MoC
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Hi-index | 0.00 |
Dataflow programming models are well-suited to program many-core streaming applications. However, many streaming applications have a dynamic behavior. To capture this behavior, parametric dataflow models have been introduced over the years. Still, such models do not allow the topology of the dataflow graph to change at runtime, a feature that is also required to program modern streaming applications. To overcome these restrictions, we propose a new model of computation, the Boolean Parametric Data Flow (BPDF) model which combines integer parameters (to express dynamic rates) and boolean parameters (to express the activation and deactivation of communication channels). High dynamism is provided by integer parameters which can change at each basic iteration and boolean parameters which can even change within the iteration. The major challenge with such dynamic models is to guarantee liveness and boundedness. We present static analyses which ensure statically the liveness and the boundedness of BDPF graphs. We also introduce a scheduling methodology to implement our model on highly parallel platforms and demonstrate our approach using a video decoder case study.