HIERARCHICAL MAPPING TECHNIQUES FOR SIGNAL PROCESSING SYSTEMS ON PARALLEL PLATFORMS

Dataflow models are widely used for expressing the functionality of digital signal processing (DSP) applications due to their useful features, such as providing formal mechanisms for description of application functionality, imposing minimal data-dependency constraints in specifications, and exposing task and data level parallelism effectively. Due to the increased complexity of dynamics in modern DSP applications, dataflow-based design methodologies require significant enhancements in modeling and scheduling techniques to provide for efficient and flexible handling of dynamic behavior. To address this problem, in this thesis, we propose an innovative framework for mode- and dynamic-parameter-based modeling and scheduling. We apply, in a systematically integrated way, the structured mode-based dataflow modeling capability of dynamic behavior together with the features of dynamic parameter reconfiguration and quasi-static scheduling. Moreover, in our proposed framework, we present a new design method called parameterized multidimensional design hierarchy mapping (PMDHM), which is targeted to the flexible, multi-level reconfigurability, and intensive real-time processing requirements of emerging dynamic DSP systems. The proposed approach allows designers to systematically represent and transform multi-level specifications of signal processing applications from a common, dataflow-based application-level model. In addition, we propose a new technique for mapping optimization that helps designers derive efficient, platform-specific parameters for application-to-architecture mapping. These parameters help to maximize system performance on state-of-the-art parallel platforms for embedded signal processing. To further enhance the scalability of our design representations and implementation techniques, we present a formal method for analysis and mapping of parameterized DSP flowgraph structures, called topological patterns, into efficient implementations. The approach handles an important class of parameterized schedule structures in a form that is intuitive for representation and efficient for implementation. We demonstrate our methods with case studies in the fields of wireless communication and computer vision. Experimental results from these case studies show that our approaches can be used to derive optimized implementations on parallel platforms, and enhance trade-off analysis during design space exploration. Furthermore, their basis in formal modeling and analysis techniques promotes the applicability of our proposed approaches to diverse signal processing applications and architectures

Exploiting parallelism within multidimensional multirate digital signal processing systems

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations

OO-IP hybrid language design and a framework approach to the GIPC

Intensional Programming is a declarative programming paradigm in which expressions are evaluated in an inherently multidimensional context space. The Lucid family of programming languages is, to this day, the only programming languages of true intensional nature. Lucid being a functional language, Lucid programs are inherently parallel and their parallelism can be efficiently exploited by the adjunction of a procedural language to increase the granularity of its parallelism, forming hybrid Lucid languages. That very wide array of possibilities raises the need for an extremely flexible programming language investigation platform to investigate on this plethora of possibilities for Intensional Programming. That is the purpose of the General Intensional Programming System (GIPSY), especially, the General Intensional Programming Compiler (GIPC) component. The modularity, reusability and extensibility aspects of the framework approach make it an obvious candidate for the development of the GIPC. The framework presented in this thesis provides a better solution compared to all other techniques used to this day to implement the different variants of intensional programming. Because of the functionality of hybrid programming support in the GIPC framework, a new OO-IP hybrid language is designed for further research. This new hybrid language combines the essential characteristics of IPL and Java, and introduces the notion of object streams which makes it is possible that each element in an IPL stream could be an object with embedded intensional properties. Interestingly, this hybrid language also brings to Java objects the power which can explicitly express context, creating the novel concept of intensional objects, Le. objects whose evaluation is context-dependent, which are therein demonstrated to be translatable into standard objects. By this new feature, we extend the use and meaning of the notion of object and enrich the meaning of stream in IPL and semantics of Java. At the same time, during the procedure to introduce intensional objects and this OO-IP hybrid language, many factors are considered. These factors include how to integrate the new language with the GIPC framework design and the issues related to its integration in the current GIPSY implementation. Current semantic rules show that the new language can work well with the GIPC framework and the GIPSY implementation, which is another proof of the validity of our GIPC framework design. Ultimately, the proposed design is put into implementation in the GIPSY and the implementation put to test using programs from different application domains written in this new OO-IP languag

Multidimensional dataflow graphs

In many signal processing applications, the tokens in a stream of tokens have a dimension higher than one. For example, the tokens in a video stream represent images so that a video application is actually three- or four-dimensional: Two dimensions are required in order to describe the pixel coordinates, one dimension indexes the different color components, and the time finally corresponds to the last dimension. Static multidimensional (MD) streaming applications can be modeled using one-dimensional dataflow graphs[7], but these are at best cyclostatic dataflow graphs, often with many phases in the actor’s vector valued token production and consumption patterns. These models incur a high control overhead. Furthermore such a notation hides many important algorithm properties such as inherent data parallelism, fine grained data dependencies and thus required memory sizes. Finally, the model is very implementation specific in that some of the degrees of freedom such as the processing order are already nailed down and cannot be changed easily without completely recreating the model

Dynamic and Multidimensional Dataflow Graphs

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