Resource optimization for CSDF-modeled streaming applications with latency constraints

Abstract

Abstract-In this paper, we study the problem of minimizing the number of processors required for scheduling latencyconstrained streaming applications modeled as CSDF graphs, where the actors of a CSDF are executed as strictly periodic tasks. We formalize the problem and prove that due to the strict periodicity of actors the problem is an integer convex programming problem, that can be solved efficiently by using an existing convex programming solver. We evaluate our solution approach on a set of 13 real-life streaming applications modeled as CSDF graphs and demonstrate that it can reduce the number of processors in more than 52% of the conducted experiments in comparison to an existing approach. I. IntroductionStreaming applications, such as video/audio processing and digital signal processing, have become prevalent in embedded systems. These applications contain ample amount of parallelism which perfectly matches the processing power of MultiProcessor System-on-Chip (MPSoC) platforms. To efficiently program MPSoC platforms, Models-of-Computation (MoCs) are used to specify streaming applications. Prominent examples of MoCs include Synchronous Data Flow (SDF) [1] and its generalization Cyclo-Static Dataflow (CSDF) [2], in which actors representing computation are executed concurrently, thereby naturally exposing parallelism. Furthermore, the strong design-time analyzability of these MoCs makes them suitable for designing performance-constrained embedded systems.Performance constraints of a streaming application are usually imposed on two principle metrics, throughput and latency. For many streaming applications, the latency is the main concern, where latency is the elapsed time between the arrival of a sample to an application and the output of the processed sample by the application. For instance, in video conferencing and automatic pattern recognition applications, a latency that exceeds a certain limit cannot be tolerated. At the same time, the required number of processors to execute the application should be minimized for better resource usage and energy efficiency.A few efforts have been made to deal with latency of streaming applications specified as SDF/CSDF graphs. The authors in [3] studied minimizing latency for SDF graphs, where latency is computed by a state-space traversal which has exponential complexity. Moreover, they assumed that there is no constraint on the number of processors required to schedule an application. However, the number of processors is an important design concern for embedded systems with respect to power consumption and area. In another effort, the authors in [4] and [5] proposed a scheduling framework that schedules acyclic CSDF graphs in a strictly periodic fashion. In this scheduling framework, each CSDF actor executes strictly periodically and meets a given deadline. The periodic execution of actors guarantees a certain

Applying Pay-Burst-Only-Once Principle for Periodic Power Management in Hard Real-Time Pipelined Multiprocessor Systems

“…There are also methods [Davare et al 2007;Hong et al 2011;Juurlink 2006, 2009;Liu et al 2014;Yu and Prasanna 2002] for hard real-time systems. To guarantee the end-to-end delay, the anthers in Liu et al [2014] studied the problem of minimizing the number of processors required for scheduling end-to-end deadlineconstrained streaming applications modeled as CSDF graphs, where the actors of a CSDF are executed as strictly periodic tasks. In Davare et al [2007], the authors optimized periods for dependent tasks on hard real-time distributed automotive systems in order to meet the end-to-end constraints.…”

DAEDALUS: System-Level Design Methodology for Streaming Multiprocessor Embedded Systems on Chips

“…In addition to the well-established methods and techniques, presented in this chapter, DAEDALUS has been extended with new advanced techniques and tools for designing hard-real-time embedded streaming MPSoCs. This extended version of DAEDALUS is called DAEDALUS RT [2][3][4][5]28,47]. Its extra features are (1) support for multiple applications running simultaneously on an MPSoC; (2) very fast, yet accurate, schedulability analysis to determine the minimum number of processors needed to schedule the applications; and (3) usage of hard-real-time multiprocessor scheduling algorithms providing temporal isolation to schedule the applications.…”

Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

“…In [10]- [12], the authors provide an analytical framework for computing timing parameters for actors of acyclic Cyclo-Static Dataflow (CSDF) applications with single-input streaming. The actors are considered as implicit-deadline and constrained-deadline periodic tasks in [10] and [11], [12], respectively.…”

“…The actors are considered as implicit-deadline and constrained-deadline periodic tasks in [10] and [11], [12], respectively. In contrast, the proposed approach is more general and can deal with any HSDF graph (CSDF can be converted to an HSDF), single/multiple input, and actors are modelled as arbitrary-deadline tasks.…”

“…Dataflow applications traditionally use static scheduling techniques, i.e. Time Division Multiple Access (TDMA) [2], [4]- [8], however, they have recently been shown to work well also with real-time scheduling techniques [9]- [12], run-time budget schedulers [13], [14] and non-starvation-free schedulers [15].…”