SPDF: A Schedulable Parametric Data-Flow MoC

International audienceDataflow programming models are suitable to express multi-core streaming applications. The design of high- quality embedded systems in that context requires static analysis to ensure the liveness and bounded memory of the application. However, many streaming applications have a dynamic behavior. The previously proposed dataflow models for dynamic applications do not provide any static guarantees or only in exchange of significant restrictions in expressive power or automation. To overcome these restrictions, we propose the schedulable parametric dataflow (SPDF) model. We present static analyses and a quasi-static scheduling algorithm. We demonstrate our approach using a video decoder case study

SPDF: A Schedulable Parametric Data-Flow MoC

International audienceDataflow programming models are suitable to express multi-core streaming applications. The design of high- quality embedded systems in that context requires static analysis to ensure the liveness and bounded memory of the application. However, many streaming applications have a dynamic behavior. The previously proposed dataflow models for dynamic applications do not provide any static guarantees or only in exchange of significant restrictions in expressive power or automation. To overcome these restrictions, we propose the schedulable parametric dataflow (SPDF) model. We present static analyses and a quasi-static scheduling algorithm. We demonstrate our approach using a video decoder case study

Execution-time Prediction for Dynamic Streaming Applications with Task-level Parallelism

Programmable multiprocessor systems-on-chip are becoming the preferred implementation platform for embedded streaming applications. This enables using more software components, which leads to large and frequent dynamic variations of data-dependent execution times. In this context, accurate and conservative prediction of execution times helps in maintaining good audio/video quality and reducing energy consumption by dynamic evaluation of the amount of on-chip resources needed by applications. To be effective, multiprocessor systems have to employ the available parallelism. The combination of task-level parallelism and task delay variations makes predicting execution times a very hard problem. So far, under these conditions, no appropriate techniques exist for the conservative prediction of execution times with the required accuracy. In this paper, we present a novel technique for this problem, exploiting the concept of scenario-based prediction, and taking into account the transient and periodic behavior of scenarios and the effect of scenario transitions. In our MPEG-4 shape-decoder case study, we observe no more than 11% average overestimation

On resource estimation of MPEG-4 video decoding for a multiprocessor architecture.

This paper addresses an efficient implementation of new emerging video algorithms like the coding of arbitrarily shaped video objects in the new MPEG-4 standard. This type of advanced multimedia applications pose challenging requirements on embedded systems design with respect to decomposition and scalability, in order to meet real-time constraints. We study the design of networks-on-chip (NoC), which intrinsically satisfies these requirements [5]. A job scheduler needs to know the worst-case execution time (WCET) of a starting job to ensure that the job can meet its timing constraints. For the purpose of timing analysis, such as computing the WCET, a timing model has been applied which has a linear dependence on a set of inputdependent data parameters. We derive a linear timing model for MPEG-4 video object decoding from a running executable specification. Our timing model is computed and verified with an instruction-set simulator of a RISC processor element containing a flat local memory model. The derived model is accurate within 6% for the average execution time