Dataflow descriptions have been used in a wide range of Digital Signal Processing (DSP) applications, such as multi-media processing, and wireless communications. Among various forms of dataflow modeling, Synchronous Dataflow (SDF) is geared towards static scheduling of computational modules, which improves system performance and predictability. However, many DSP applications do not fully conform to the restrictions of SDF modeling. More general dataflow models, such as CAL [1], have been developed to describe dynamically-structured DSP applications. Such generalized models can express dynamically changing functionality, but lose the powerful static scheduling capabilities provided by SDF. This paper focuses on detection of SDF-like regions in dynamic dataflow descriptions -in particular, in the generalized specification framework of CAL. This is an important step for applying static scheduling techniques within a dynamic dataflow framework. Our techniques combine the advantages of different dataflow languages and tools, including CAL [1], DIF [2] and CAL2C [3]. The techniques are demonstrated on the IDCT module of MPEG Reconfigurable Video Coding (RVC).
Data compression techniques have extensive applications in power-constrained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression for acoustic signals. Such signal processing is relevant in a variety of sensor network applications, including surveillance and monitoring. Applying data compression in a sensor node generally reduces the energy consumption of the transceiver at the expense of additional energy expended in the embedded processor due to the computational cost of compression. This paper introduces a methodology for comparing data compression algorithms in sensor networks based on the figure of merit D/E, where D is the amount of data (before compression) that can be transmitted under a given energy budget E for computation and communication. We develop experiments to evaluate, using this figure of merit, different variants of linear predictive coding. We also demonstrate how different models of computation applied to the embedded software design lead to different degrees of processing efficiency, and thereby have significant effect on the targeted figure of merit.Index Terms-DSP software, lossless data compression, linear predictive coding, low power design, wireless sensor networks.
Abstract-Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. MPC is often computation intensive, which limits the class of systems to which it can be applied and the performance criteria it can use. This paper describes a general framework called reactive, control-integrated dataflow modeling for analyzing and improving the algorithms used for MPC and their hardware implementations. The utility of the framework is demonstrated by applying it to the Newton-KKT algorithm. The results show significant reductions in computation time for test cases.
Dataflow descriptions have been used in a wide range of Digital Signal Processing (DSP) applications, such as multi-media processing, and wireless communications. Among various forms of dataflow modeling, Synchronous Dataflow (SDF) is geared towards static scheduling of computational modules, which improves system performance and predictability. However, many DSP applications do not fully conform to the restrictions of SDF modeling. More general dataflow models, such as CAL [1], have been developed to describe dynamically-structured DSP applications. Such generalized models can express dynamically changing functionality, but lose the powerful static scheduling capabilities provided by SDF. This paper focuses on detection of SDF-like regions in dynamic dataflow descriptions -in particular, in the generalized specification framework of CAL. This is an important step for applying static scheduling techniques within a dynamic dataflow framework. Our techniques combine the advantages of different dataflow languages and tools, including CAL [1], DIF [2] and CAL2C [3]. The techniques are demonstrated on the IDCT module of MPEG Reconfigurable Video Coding (RVC).
Abstract-Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. An important limitation on the application of MPC is the difficulty in completing the necessary computations within the sampling interval. Recent trends in computing hardware towards greatly increased parallelism offer a solution to this problem. This paper describes modeling and analysis tools to facilitate implementing the MPC algorithms on parallel computers, thereby greatly reducing the time needed to complete the calculations. The use of these tools is illustrated by an application to a class of MPC problems.
Abstract-Dataflow representations of Digital Signal Processing (DSP) software have been developing since the 1980's. They have proven to be useful in identifying bottlenecks in DSP algorithms, improving the efficiency of the computations, and in designing appropriate hardware for implementing the algorithms. This paper demonstrates the use of dataflow to improve a Model Predictive Control (MPC) algorithm. MPC has been extensively used in the real world but its application has been limited to relatively slow processes because it is computationally intensive. It is shown that the time required for the MPC computations using a representative active set method for solving the optimization problem can be reduced by means of dataflow analysis and implementation improvements based on these analyses.
Abstract-This paper presents an in-depth case study on dataflow-based analysis and exploitation of parallelism in the design and implementation of an MPEG RVC (reconfigurable video coding) decoder. Dataflow descriptions have been used in a wide range of digital signal processing (DSP) applications, such as applications for multimedia processing and wireless communications. Because dataflow models are effective in exposing concurrency and other important forms of high level application structure, dataflow techniques are promising for implementing complex DSP applications on multi-core systems, and other kinds of parallel processing platforms. In this paper, we use the CAL language as a concrete framework for representing and demonstrating dataflow design techniques.Furthermore, we also describe our application of The DIF package (TDP), a software tool for analyzing dataflow networks, to the systematic exploitation of concurrency in CAL networks that are targeted to multi-core platforms. Using TDP, one is able to automatically process regions that are extracted from the original network, and exhibit properties similar to synchronous dataflow (SDF) models. This is important in our context because powerful techniques, based on static scheduling, are available for exploiting concurrency in SDF descriptions. Detection of SDFlike regions is an important step for applying static scheduling techniques within a dynamic dataflow framework. Furthermore, segmenting a system into SDF-like regions also allows us to explore cross-actor concurrency that results from dynamic dependencies among different regions. Using SDF-like region detection as a preprocessing step to software synthesis generally provides an efficient way for mapping tasks to multi-core systems, and improves the system performance of video processing applications on multi-core platforms.
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