OpenDF - A Dataflow Toolset for Reconfigurable Hardware and Multicore Systems

International audienceThis paper presents the OpenDF framework and recalls that dataflow programming was once invented to address the problem of parallel computing. We discuss the problems with an imperative style, von Neumann programs, and present what we believe are the advantages of using a dataflow programming model. The CAL actor language is briefly presented and its role in the ISO/MPEG standard is discussed. The Dataflow Interchange Format (DIF) and related tools can be used for analysis of actors and networks, demonstrating the advantages of a dataflow approach. Finally, an overview of a case study implementing an MPEG-4 decoder is given

MPEG Reconfigurable Video Coding: From specification to a reconfigurable implementation

International audienceThis paper demonstrates that it is possible to produce automatic, reconfigurable, and portable implementations of multimedia decoders onto platforms with the help of the MPEG Reconfigurable Video Coding (RVC) standard. MPEG RVC is a new formalism standardized by the MPEGconsortium used to specify multimedia decoders. It produces visual representations of decoder reference software, with the help of graphs that connect several coding tools from MPEG standards. The approach developed in this paper draws on Dataflow Process Networks to produce a Minimal and Canonical Representation (MCR) of \MPEG\ \RVC\ specifications. The \MCR\ makes it possible to form automatic and reconfigurable implementations of decoders which can match any actual platforms. The contribution is demonstrated on one case study where a generic decoder needs to process a multimedia content with the help of the \RVC\ specification of the decoder required to process it. The overall approach is tested on two decoders from MPEG, namely MPEG-4 part 2 Simple Profile and MPEG-4 part 10 Constrained Baseline Profile. The results validate the following benefits on the \MCR\ of decoders: compact representation, low overhead induced by its compilation, reconfiguration and multi-core abilities

OpenDF - A Dataflow Toolset for Reconfigurable Hardware and Multicore Systems

This paper presents the OpenDF framework and recalls that dataflow programming was once invented to address the problem of parallel computing. We discuss the problems with an imperative style, von Neumann programs, and present what we believe are the advantages of using a dataflow programming model. The CAL actor language is briefly presented and its role in the ISO/MPEG standard is discussed. The Dataflow Interchange Format (DIF) and related tools can be used for analysis of actors and networks, demonstrating the advantages of a dataflow approach. Finally, an overview of a case study implementing an MPEG-4 decoder is given

OpenDF – A Dataflow Toolset for Reconfigurable Hardware and Multicore Systems

This paper presents the OpenDF framework and recalls that dataflow programming was once invented to address the problem of parallel computing. We discuss the problems with an imperative style, von Neumann programs,and present what we believe are the advantages of using a briefly presented and its role in the ISO/MPEG standard is discussed. The Dataflow Interchange Format (DIF) and related tools can be used for analysis of actors and networks,demonstrating the advantages of a dataflow approach. Finally, an overview of a case study implementing an MPEG-4 decoder is given

Algorithm/Architecture Co-Exploration of Visual Computing: Overview and Future Perspectives

Concurrently exploring both algorithmic and architectural optimizations is a new design paradigm. This survey paper addresses the latest research and future perspectives on the simultaneous development of video coding, processing, and computing algorithms with emerging platforms that have multiple cores and reconfigurable architecture. As the algorithms in forthcoming visual systems become increasingly complex, many applications must have different profiles with different levels of performance. Hence, with expectations that the visual experience in the future will become continuously better, it is critical that advanced platforms provide higher performance, better flexibility, and lower power consumption. To achieve these goals, algorithm and architecture co-design is significant for characterizing the algorithmic complexity used to optimize targeted architecture. This paper shows that seamless weaving of the development of previously autonomous visual computing algorithms and multicore or reconfigurable architectures will unavoidably become the leading trend in the future of video technology

Classification-Based Optimization of Dynamic Dataflow Programs

International audienceThis chapter reviews dataflow programming as a whole and presents a classification-based methodology to bridge the gap between predictable and dynamic dataflow modeling in order to achieve expressiveness of the programming language as well as efficiency of the implementation. The authors conduct experiments across three MPEG video decoders including one based on the new High Efficiency Video Coding standard. Those dataflow-based video decoders are executed onto two different platforms: a desktop processor and an embedded platform composed of interconnected and tiny Very Long Instruction Word-style processors. The authors show that the fully automated transformations presented can result in a 80% gain in speed compared to runtime scheduling in the more favorable case

Efficient Software Implementation of Stream Programs

The way we use computers and mobile phones today requires large amounts of processing of data streams. Examples include digital signal processing for wireless transmission, audio and video coding for recording and watching videos, and noise reduction for the phone calls. These tasks can be performed by stream programs—computer programs that process streams of data. Stream programs can be composed of other stream programs. Components of a composition are connected in a network, i.e. the output streams of one component are sent as input streams to other components. The components, that perform the actual computation, are called kernels. They can be described in different styles and programming languages. There are also formal models for describing the kernels and the networks. One such model is the actor machine.This dissertation evaluates the actor machine, how it facilitates creating efficient software implementation of stream programs. The evaluation is divided into four aspects: (1) analyzability of its structure, (2) generality in what languages and styles it can express, (3) efficient implementation of kernels, and (4) efficient implementation of networks. This dissertation demonstrates all four aspects through implementation and evaluation of a stream program compiler based on actor machines

Improving Model-Based Software Synthesis: A Focus on Mathematical Structures

Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis. We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general. This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking. We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability. The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures

A Dataflow Framework For Developing Flexible Embedded Accelerators A Computer Vision Case Study.

The focus of this dissertation is the design and the implementation of a computing platform which can accelerate data processing in the embedded computation domain. We focus on a heterogeneous computing platform, whose hardware implementation can approach the power and area efficiency of specialized designs, while remaining flexible across the application domain. The multi-core architectures require parallel programming, which is widely-regarded as more challenging than sequential programming. Although shared memory parallel programs may be fairly easy to write (using OpenMP, for example), they are quite hard to optimize; providing embedded application developers with optimizing tools and programming frameworks is a challenge. The heterogeneous specialized elements make the problem even more difficult. Dataflow is a parallel computation model that relies exclusively on message passing, and that has some advantages over parallel programming tools in wide use today: simplicity, graphical representation, and determinism. Dataflow model is also a good match to streaming applications, such as audio, video and image processing, which operate on large sequences of data and are characterized by abundant parallelism and regular memory access patterns. Dataflow model of computation has gained acceptance in simulation and signal-processing communities. This thesis evaluates the applicability of the dataflow model for implementing domain-specific embedded accelerators for streaming applications

Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe