and MICHAEL MEREDITH Forte Design SystemsWith increasing design complexity, the gap from ESL (Electronic System Level) design to RTL synthesis becomes more and more crucial to many industrial projects. Although several behavioral synthesis tools exist to automatically generate synthesizable RTL code from C/C++/SystemCbased input descriptions and software generation for embedded processors is automated as well, an efficient ESL synthesis methodology combining both is still missing. This article presents SYS-TEMCODESIGNER, a novel SystemC-based ESL tool to automatically optimize a hardware/software SoC (System on Chip) implementation with respect to several objectives. Starting from a SystemC behavioral model, SYSTEMCODESIGNER automatically extracts the mathematical model, performs a behavioral synthesis step, and explores the multiobjective design space using state-of-the-art multiobjective optimization algorithms. During design space exploration, a single design point is evaluated by simulating highly accurate performance models, which are automatically generated from the SystemC behavioral model and the behavioral synthesis results. Moreover, SYSTEMCODESIGNER permits the automatic generation of bit streams for FPGA targets from any previously optimized SoC implementation. Thus SYSTEMCODESIGNER is the first fully automated ESL synthesis tool providing a correct-by-construction generation of hardware/software SoC implementations. As a case study, a model of a Motion-JPEG decoder was automatically optimized and implemented using SYSTEMCODESIGNER. Several synthesized SoC variants based on this model show different tradeoffs between required hardware costs and achieved system throughput, ranging from software-only solutions to pure hardware implementations that reach real-time performance for QCIF streams on a 50MHz FPGA. ACM Reference Format:Keinert, J., Streubühr, M., Schlichter, T., Falk, J., Gladigau, J., Haubelt, C., and Teich, J. 2009. SYSTEMCODESIGNER-An automatic ESL synthesis approach by design space exploration and behavioral synthesis for streaming application.

In this paper, we propose a generalized clustering approach for static data flow subgraphs mapped onto individual processors in Multi-Processor System on Chips (MPSoCs). The goal of clustering is to replace the static data flow subgraph by a single dynamic data flow actor such that the global performance in terms of latency and throughput is optimized. Through our proposed clustering approach, the scheduling of connected static data flow subgraphs can be coordinated with enclosing system representations in a way that systematically exploits the predictability and efficiency of the static data flow model. Thus, the advantages of static data flow subsystems can be exploited in the context of overall system representations that are based on more general models of computation. At the same time, our approach goes significantly beyond previous approaches to synchronous data flow clustering by providing a quasi-static -as opposed to purely-static -scheduling interface between clustered subgraphs and the enclosing systems. This greatly enhances the power of our techniques in terms of avoiding deadlock, increasing the design space for clustering, and providing for integration with more general models of computation. We show benefits of up to 95% performance improvement for real world examples.

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Much of the work to date on data ow models for signal processing system design has focused decidable data ow models that are best suited for onedimensional signal processing. In this chapter, we review more general data ow modeling techniques that are targeted to applications that include multidimensional signal processing and dynamic data ow behavior. As data ow techniques are applied to signal processing systems that are more complex, and demand increasing degrees of agility and exibility, these classes of more general data ow models are of correspondingly increasing interest. We begin with a discussion of two data ow modeling techniques -multi-dimensional synchronous data ow and windowed data ow -that are targeted towards multidimensional signal processing applications. We then provide a motivation for dynamic data ow models of computation, and review a number of speci c methods that have emerged in this class of models. Our coverage of dynamic data ow models in this chapter includes Boolean data ow, the stream-based function model, CAL, parameterized data ow, and enable-invoke data ow.

Sliding window algorithms are fundamental parts of each image processing system. Especially those belonging to the class of static algorithms offer various possibilities for analysis and optimization. Only if this potential is exploited, a high level synthesis of such algorithms will lead to efficient implementations.Such an analysis relies on an efficient representation by a welldefined model of computation. It must abstract important properties of sliding windows as for instance the relation between input and output data as well as the required buffer space. In this paper, a corresponding static model of computation for sliding window algorithms is elaborated, called Windowed Synchronous Data Flow (WSDF). Its main focus lies on applications with two or more dimensions. Furthermore, the WSDF balance equation is derived allowing to verify bounded token accumulation during execution.

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Figure 1: Our real-time multi-view correspondence algorithm extracts multi-view depth maps from sparse, wide-baseline light field video (here 3×3 cameras), in order to produce high-quality novel views for applications such as virtual apertures or virtual camera positions. AbstractLight field videos express the entire visual information of an animated scene, but their shear size typically makes capture, processing and display an off-line process, i. e., time between initial capture and final display is far from real-time. In this paper we propose a solution for one of the key bottlenecks in such a processing pipeline, which is a reliable depth reconstruction possibly for many views. This is enabled by a novel correspondence algorithm converting the video streams from a sparse array of off-the-shelf cameras into an array of animated depth maps. The algorithm is based on a generalization of the classic multi-resolution Lucas-Kanade correspondence algorithm from a pair of images to an entire array. Special inter-image confidence consolidation allows recovery from unreliable matching in some locations and some views. It can be implemented efficiently in massively parallel hardware, allowing for interactive computations. The resulting depth quality as well as the computation performance compares favorably to other state-of-the art light field-to-depth approaches, as well as stereo matching techniques. Another outcome of this work is a data set of light field videos that are captured with multiple variants of sparse camera arrays.