Heterogeneous multi-processor for the management of real-time video and graphics streams

Strik, M.; Timmer, A.; Meerbergen, J. van; Waterlander, E.; Harmsze, F.; Vaassen, A.; Sevat, L.; Oosterhuis, M.; Rootselaar, G.J. van; Herten, H. van; Jaspers, E.; Janssen, J.; Essink, G.; Leijten, J.

doi:10.1109/isscc.2000.839769

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…only if these decisions depend on values in the processed stream. Semi-static assignment (Strik et al 2000;Moreira et al 2005) is a scheduling strategy that is in between static assignment and fully dynamic. In semi-static assignment, the processor assignment is done at the time a job is started by the user and remains unchanged while the job is executing.…”

Section: Multiprocessor Schedulingmentioning

confidence: 99%

Aperiodic multiprocessor scheduling for real-time stream processing applications

Wiggers¹

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This thesis is concerned with the computation of buffer capacities that guarantee satisfaction of timing and resource constraints for task graphs with aperiodic task execution rates that are executed on run-time scheduled resources. Stream processing applications such as digital radio baseband processing and audio or video decoders are often firm real-time embedded systems. For a real-time embedded system, guarantees on the satisfaction of timing constraints are based on a model. This model forms a load hypothesis. In contrast to hard real-time embedded systems, firm real-time embedded systems have no safety requirements. However, firm real-time embedded systems have to be designed to tolerate the situation that the load hypothesis is inadequate. For stream processing applications, a deadline miss can lead to a drastic reduction in the perceived quality, for instance the loss of synchronisation with the radio stream in case of a digital radio can result in a loss of audio for seconds. For power and performance reasons, stream processing applications typically require a multiprocessor system, on which these applications are implemented as task graphs, with tasks communicating data values over buffers. The established time-triggered and event-triggered design paradigms for real-time embedded systems are not applicable. This is because timetriggered systems are not tolerant to an inadequate load hypothesis, for example non-conservative worst-case execution times, and event-triggered systems have no temporal isolation from their environment. Therefore, we introduce our data-driven approach. In our data-driven approach, the interfaces with the environment are time-triggered, while the tasks that implement the functionality are data-driven. This results in a system where guarantees on the satisfaction of the timing constraints can be provided given that the load hypothesis is adequate. If the load hypothesis is inadequate, then for instance non-conservative worst-case execution times do not immediately result in corrupted data values and undefined functional v vi ABSTRACT behaviour. Stream processing applications are increasingly adaptive to their environment, for example digital radios that adapt to the channel condition. This adaptivity results in increasingly intricate sequential control in stream processing applications. The implementations of stream processing applications as task graphs, consequently, have inter-task synchronisation behaviour that is dependent on the processed data stream. Currently, cyclostatic dataflow is the most expressive model that is applicable in our datadriven approach and that can provide guarantees on the satisfaction of timing constraints. However, cyclo-static dataflow cannot express inter-task synchronisation behaviour that is dependent on the processed data stream. Boolean dataflow can express inter-task synchronisation behaviour that is dependent on the processed data stream. However, for boolean dataflow, and models with similar expressiveness, deadlock-freedom is an undecidable ...

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Section: Multiprocessor Schedulingmentioning

confidence: 99%