RVC-CAL dataflow implementations of MPEG AVC/H.264 CABAC decoding

Bezati, Endri; Mattavelli, Marco; Raulet, Mickaël

doi:10.1109/dasip.2010.5706266

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…Significant work exists on parallelizing H.264 and its successor HEVC [41], [42], and some of this work explicitly addresses the issue of statically parallelizing the decoding process. However, existing work mainly considers architectures where the frames used for motion compensation are stored in a central memory, known as the frame buffer, which the decoding threads that run in parallel access concurrently.…”

Section: Resultsmentioning

confidence: 99%

Reconciling performance and predictability on a many-core through off-line mapping

Carle

Djemal

Genius

et al. 2014

2014 9th International Symposium on Reconfigurable and Communication-Centric Systems-on-Chip (ReCoSoC)

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Abstract-We start from a general-purpose many-core architecture designed for average-case performance and ease of use. In particular, its distributed shared memory programming model allows the use of a code generation flow based on the (unmodified) gcc compiler chain. We modify this architecture and extend the code generation flow to allow the construction of efficient hard real-time systems starting from dependent task specifications. We rely on a static (off-line) real-time scheduling paradigm welladapted to embedded control and signal processing applications with regular control structure.We modify the architecture (and in particular the on-chip network) to allow the implementation of static schedules with very high (clock cycle) temporal precision. On the software side, we define application mapping rules ensuring that the timing precision provided by the hardware is not lost. These mapping rules include requirements on the allocation of data variables to specific RAM banks and on the use of locks to ensure the absence of contentions during access to shared resources. Applications complying with these rules can be written manually or automatically obtained using a new mapping tool that takes all the allocation and scheduling decisions. Compilation of the resulting C code is still done using the (unmodified) gcc compiler chain. The resulting platform provides good performance, and at the same provides very high timing precision, as shown by two case studies (an embedded controller and an implementation of the FFT).We conclude our paper with a presentation of some ongoing work on the subject: A case study (an implementation of the H.264 decoder) meant to test the limitations of our method.

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Section: Resultsmentioning

confidence: 99%

Reconciling performance and predictability on a many-core through off-line mapping

Carle

Djemal

Genius

et al. 2014

2014 9th International Symposium on Reconfigurable and Communication-Centric Systems-on-Chip (ReCoSoC)

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“…Indeed, stream-processing algorithms naturally exhibit dataflow graphs, which simplifies the application design and task implementation. Recent publications like [1,7] show successful demonstrations of the efficiency of dataflow languages to implement video decoders.…”

Section: Introductionmentioning

confidence: 98%

A novel approach for interactive debugging of dynamic dataflow embedded applications

Pouget

Santana

Cueva

et al. 2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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In this paper, we propose a new approach for source-level dataflow debuggers. Going beyond their long-established ability to support sequential programming languages, we describe the functionalities a debugger should be able to provide to debug embedded and parallel dataflow applications. Then we demonstrate our solution to this problem with a proof-of-concept debugger targeting the dataflow framework used on an industrial MPSoC platform. We also explain the development challenges we faced during the implementation of this GDB-based debugger.

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“…It is a data-centric model and can be represented as a graph, where nodes are the operations that are applied to the incoming data, and edges represent the flow of data. It is used in different domains like signal processing [2], [3], [4], [5] and image [6] or video processing [7]. New languages based on this approach have also been developed [8], [9], [10] and [11].…”

Section: Introductionmentioning

confidence: 99%

Tracing and Profiling Machine Learning Dataflow Applications on GPU

Zins

Dagenais

2019

Int J Parallel Prog

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In this paper, we propose a profiling and tracing method for dataflow applications with GPU acceleration. Dataflow models can be represented by graphs and are widely used in many domains like signal processing or machine learning. Within the graph, the data flows along the edges, and the nodes correspond to the computing units that process the data. To accelerate the execution, some co-processing units, like GPUs, are often used for computing intensive nodes. The work in this paper aims at providing useful information about the execution of the dataflow graph on the available hardware, in order to understand and possibly improve the performance. The collected traces include low level information about the CPU, from the Linux Kernel, as well as high-level information about the dataflow model. This is followed by post-mortem analysis and visualization steps in order to enhance the trace and show useful information to the user. To demonstrate the effectiveness of the method, it was evaluated for TensorFlow, a well-known machine learning library that uses a dataflow computational graph to represent the algorithms. We present a few examples of machine learning applications that can be optimized with the help of the information provided by our proposed method.

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RVC-CAL dataflow implementations of MPEG AVC/H.264 CABAC decoding

Cited by 11 publications

References 5 publications

Reconciling performance and predictability on a many-core through off-line mapping

Reconciling performance and predictability on a many-core through off-line mapping

A novel approach for interactive debugging of dynamic dataflow embedded applications

Tracing and Profiling Machine Learning Dataflow Applications on GPU

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