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.
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