A platform-independent methodology for performance estimation of streaming media applications

Stolberg, H.-J.; Berekovic, Mladen; Pirsch, P.

doi:10.1109/icme.2002.1035523

Cited by 15 publications

(12 citation statements)

References 9 publications

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“…Application Analysis. System Level Design In order to estimate the processing demands of a streaming media application, a platformindependent performance analysis has to be performed [11,12]. From the analysis we derive a complexity profile which is characteristic for a specific application.…”

Section: Figmentioning

confidence: 99%

RAPANUI: Rapid Prototyping for Media Processor Architecture Exploration

Vayá

Langerwerf

Pirsch

2005

Lecture Notes in Computer Science

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Abstract. This paper describes a new rapid prototyping-based design framework for exploring and validating complex multiprocessor architectures for multimedia applications. The new methodology combines a typical ASIC flow with an FPGA flow focused on rapid prototyping. In order to make an exhaustive verification of the system architecture, a reference model that specifies the hardware implementation is used for validating both, HDL description and emulated system. Functional coverage in addition to traditional code coverage is used to test 100% of data, control and structural hazards of the system architecture. The reference model is also part of a stand-alone simulation environment. This allows hardware and application development be supported by a unique system model.

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Section: Figmentioning

confidence: 99%

RAPANUI: Rapid Prototyping for Media Processor Architecture Exploration

Vayá

Langerwerf

Pirsch

2005

Lecture Notes in Computer Science

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“…However, taking the influence of the architecture into account is very important for a system architecture exploration, as also considered in [9].…”

Section: Memtrace: a Performancementioning

confidence: 99%

“…However, all these tools cannot provide timing analysis due to the level of abstraction, as they either use an abstract hardware architecture [5], [6] or are restricted to dataflow analysis [7], [8]. In [9], a methodology is presented which combines high level profiling with a cycle-accurate timing database. It profiles the number of executions of algorithmic processing kernels, such as inverse discrete cosine transform (IDCT), of an application and creates platform specific performance estimation by database mapping of these numbers to their execution times on the specific platform.…”

Section: Introductionmentioning

confidence: 99%

Profiling-Based Hardware/Software Co-Exploration for the Design of Video Coding Architectures

Hübert

Stabernack

2009

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-The design of embedded hardware/software systems is often subject to strict requirements concerning its various aspects, including real-time performance, power consumption, and die area. Especially for data intensive applications, the number of memory accesses is a dominant factor for these aspects. In order to meet the requirements and design a welladapted system, the software parts need to be optimized and an adequate system and processor architecture needs to be designed. In this paper, we focus on finding an optimized memory hierarchy for bus-based architectures. Additionally, useful instruction set extensions for application-specific processor cores are explored. For complex applications, this design space exploration is difficult and requires in-depth analysis of the application and its implementation alternatives. Tools are required which aid the designer in the design, optimization, and scheduling of hardware and software. We present a profiling tool for fast and accurate performance, power, and memory access analysis of embedded systems. This paper shows how the tool can be applied for an efficient hardware/software co-exploration within the design flow of processor-centric architectures. This concept has been proven in the design of a mixed hardware/software system with multiple processing units for video decoding.

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“…where the case of NLMS corresponds to (12), while LMS is the case where the simplification of (13) is used. In (18) For complexity prediction with GCMs at the decoder side, the server must transmit…”

Section: Iiic Complexity and Information Requirements For Adaptive mentioning

confidence: 99%

“…Stolberg et al [12] use a mixed methodology based on prediction techniques and off-line tool-based analysis and in more recent work [13] propose a generic approach for off-line or on-line linear mapping of critical function executions to the observed timing complexity. He et al [14] proposed an analytic model for power/rate/distortion estimation of hybrid video encoding and investigated several tradeoffs using dynamic voltage scaling techniques.…”

Section: Ia Related Workmentioning

confidence: 99%

Adaptive Linear Prediction for Resource Estimation of Video Decoding

Andreopoulos

Schaar

2007

IEEE Trans. Circuits Syst. Video Technol.

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Current systems often assume "worst-case" resource utilization for the design and implementation of compression techniques and standards, thereby neglecting the fact that multimedia coding algorithms require time-varying resources, which differ significantly from the "worst-case" requirements. To enable adaptive resource management for multimedia systems, resource-estimation mechanisms are needed. Previous research demonstrated that on-line adaptive linear prediction techniques typically exhibit superior efficiency to other alternatives for resource prediction of multimedia systems. In this paper, we formulate the problem of adaptive linear prediction of video decoding resources by analytically expressing the possible adaptation parameters for a broad class of video decoders. The resources are measured in terms of the time required for a particular operation of each decoding unit (e.g. motion compensation or entropy decoding of a video frame). Unlike prior research that mainly focuses on estimation of execution time based on previous measurements (i.e. based on autoregressive prediction or platform and decoder-specific off-line training), we propose the use of generic complexity metrics (GCMs) as the input for the adaptive predictor. GCMs represent the number of times the basic building blocks are executed by the decoder and depend on the source characteristics, decoding bit-rate and the specific algorithm implementation. Different GCM granularities (e.g. per video frame or macroblock) are explored. Our previous research indicated that GCMs can be measured or modeled at the encoder or the video server side and they can be streamed to the decoder along with the compressed bitstream. A comparison of GCM-based versus autoregressive adaptive prediction over a large range of adaptation parameters is performed. Our results indicate that GCM-based prediction is significantly-superior to the autoregressive approach and also requires less computational resources at the decoder. As a result a novel resource-prediction tradeoff is explored between: (a) the communication overhead for GCMs and/or the implementation overhead for the realization of the predictor, and (b) the improvement of the prediction performance. Since this trade-off can be significant for the decoder platform (either from the communication or the implementation perspective), we propose complexity (or communication) bounded adaptive linear prediction in order to derive the best resource estimation under the given implementation (or GCM-communication) bound.

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A platform-independent methodology for performance estimation of streaming media applications

Cited by 15 publications

References 9 publications

RAPANUI: Rapid Prototyping for Media Processor Architecture Exploration

RAPANUI: Rapid Prototyping for Media Processor Architecture Exploration

Profiling-Based Hardware/Software Co-Exploration for the Design of Video Coding Architectures

Adaptive Linear Prediction for Resource Estimation of Video Decoding

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