Sander Sander Stuijk

SDF^3: SDF For Free

SDF^3 is a tool for generating random Synchronous DataFlow Graphs (SDFGs), if desirable with certain guaranteed properties like strongly connectedness. It includes an extensive library of SDFG analysis and transformation algorithms as well as functionality to visualize them. The tool can create SDFG benchmarks that mimic DSP or multimedia applications.

Throughput Analysis of Synchronous Data Flow Graphs

Synchronous data flow graphs (SDFGs) are a useful tool for modeling and analyzing embedded data flow applications, both in a single processor and a multiprocessing context or for application mapping on platforms. Throughput analysis of these SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, the size of which can be exponentially larger than the size of the original graph. In this paper, we present a method for throughput analysis of SDFGs, based on explicit state-space exploration and we show that the method, despite its worst-case complexity, works well in practice, while existing methods often fail. We demonstrate this by comparing the method with state-of-the-art cycle mean computation algorithms. Moreover, since the state-space exploration method is essentially the same as simulation of the graph, the results of this paper can be easily obtained as a byproduct in existing simulation tools

Exploring trade-offs in buffer requirements and throughput constraints for synchronous dataflow graphs

Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as synchronous dataflow graphs. Communication between nodes in these graphs requires storage space which influences throughput. We present exact techniques to chart the Pareto space of throughput and storage tradeoffs, which can be used to determine the minimal storage space needed to execute a graph under a given throughput constraint. The feasibility of the approach is demonstrated with a number of examples

Multiprocessor resource allocation for throughput-constrained synchronous dataflow graphs

Embedded multimedia systems often run multiple time-constrained applications simultaneously. These systems use multiprocessor systems-on-chip of which it must be guaranteed that enough resources are available for each application to meet its throughput constraints. This requires a task binding and scheduling mechanism that provides timing guarantees for each application independent of other applications while taking into account the available processor space, memory and communication bandwidth. Synchronous dataflow graphs (SDFGs) are used to model time-constrained multimedia applications. They allow modeling of cyclic, multi- rate dependencies between tasks. However, existing resource allocation techniques can only deal with acyclic and/or single-rate dependencies. Dependencies in an SDFG can be expressed in single-rate form, but then the problem size may increase exponentially making resource allocation infeasible. This paper presents a new resource allocation strategy which works directly on SDFGs, building on an efficient technique to calculate throughput of a bound and scheduled SDFG. Experimental results show that the strategy is effective in terms of run-time and allocated resources.

A scenario-aware data flow model for combined long-run average and worst-case performance analysis

Data flow models are used for specifying and analysing signal processing and streaming applications. However, traditional data flow models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the synchronous data flow model, which uses a stochastic approach to model the order in which scenarios occur. The formally defined operational semantics of a scenario-aware data flow model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using scenario-aware data flow models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example

Throughput-Buffering Trade-Off Exploration for Cyclo-Static and Synchronous Dataflow Graphs

Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as cyclo-static or synchronous dataflow graphs. Communication between nodes in these graphs requires storage space which influences throughput. We present an exact technique to chart the Pareto space of throughput and storage trade-offs, which can be used to determine the minimal buffer space needed to execute a graph under a given throughput constraint. The feasibility of the exact technique is demonstrated with experiments on a set of realistic DSP and multimedia applications. To increase scalability of the approach, a fast approximation technique is developed that guarantees both throughput and a, tight, bound on the maximal overestimation of buffer requirements. The approximation technique allows to trade off worst-case overestimation versus run-time.

Minimising buffer requirements of synchronous dataflow graphs with model checking

Signal processing and multimedia applications are often implemented on resource constrained embedded systems. It is therefore important to find implementations that use as little resources as possible. These applications are frequently specified as synchronous data flow graphs. Communication between actors of these graphs requires storage capacity. In this paper, we present an exact method to determine the minimum storage capacity required to execute the graph using model-checking techniques. This can be done for different measures of storage capacity. The problem is known to be NP-complete and because of this, existing buffer minimisation techniques are heuristics and hence not exact. Modern model-checking tools are quite efficient and they have been successfully applied to scheduling-related problems. We study the feasibility of this approach with examples.

Exploiting Spatial Redundancy of Image Sensor for Motion Robust rPPG

Remote photoplethysmography (rPPG) techniques can measure cardiac activity by detecting pulse-induced color variations on human skin using an RGB camera. State-of-the-art rPPG methods are sensitive to subject body motions (e.g., motion-induced color distortions). This study proposes a novel framework to improve the motion robustness of rPPG. The basic idea of this paper originates from the observation that a camera can simultaneously sample multiple skin regions in parallel, and each of them can be treated as an independent sensor for pulse measurement. The spatial redundancy of an image sensor can thus be exploited to distinguish the pulse signal from motion-induced noise. To this end, the pixel-based rPPG sensors are constructed to estimate a robust pulse signal using motion-compensated pixel-to-pixel pulse extraction, spatial pruning, and temporal filtering. The evaluation of this strategy is not based on a full clinical trial, but on 36 challenging benchmark videos consisting of subjects that differ in gender, skin types, and performed motion categories. Experimental results show that the proposed method improves the SNR of the state-of-the-art rPPG technique from 3.34 to 6.76 dB, and the agreement (±1.96σ) with instantaneous reference pulse rate from 55% to 80% correct. ANOVA with post hoc comparison shows that the improvement on motion robustness is significant. The rPPG method developed in this study has a performance that is very close to that of the contact-based sensor under realistic situations, while its computational efficiency allows real-time processing on an off-the-shelf computer.

Scenario-aware dataflow: Modeling, analysis and implementation of dynamic applications

Embedded multimedia and wireless applications require a model-based design approach in order to satisfy stringent quality and cost constraints. The Model-of-Computation (MoC) should appropriately capture system dynamics, support analysis and synthesis, and allow low-overhead model-driven implementations. This combination poses a significant challenge. The Scenario-Aware DataFlow (SADF) MoC has been introduced to address this challenge. This paper surveys SADF, and compares dataflow MoCs in terms of their ability to capture system dynamics, their support for analysis and synthesis, and their implementation efficiency.

Automatic scenario detection for improved WCET estimation

Modern embedded applications usually have real-time constraints and they are implemented using heterogeneous multiprocessor systems-on-chip. Dimensioning a system requires accurate estimations of the worst-case execution time (WCET). Overestimation leads to over-dimensioning. This paper introduces a method for automatic discovery of scenarios that incorporate correlations between different parts of applications. It is based on the application parameters with a large impact on the execution time. The authors showed on a benchmark that, using scenarios, the estimated WCET might be reduced with 16%.