Modeling and Mapping of Optimized Schedules for Embedded Signal Processing Systems

The demand for Digital Signal Processing (DSP) in embedded systems has been increasing rapidly due to the proliferation of multimedia- and communication-intensive devices such as pervasive tablets and smart phones. Efficient implementation of embedded DSP systems requires integration of diverse hardware and software components, as well as dynamic workload distribution across heterogeneous computational resources. The former implies increased complexity of application modeling and analysis, but also brings enhanced potential for achieving improved energy consumption, cost or performance. The latter results from the increased use of dynamic behavior in embedded DSP applications. Furthermore, parallel programming is highly relevant in many embedded DSP areas due to the development and use of Multiprocessor System-On-Chip (MPSoC) technology. The need for efficient cooperation among different devices supporting diverse parallel embedded computations motivates high-level modeling that expresses dynamic signal processing behaviors and supports efficient task scheduling and hardware mapping. Starting with dynamic modeling, this thesis develops a systematic design methodology that supports functional simulation and hardware mapping of dynamic reconfiguration based on Parameterized Synchronous Dataflow (PSDF) graphs. By building on the DIF (Dataflow Interchange Format), which is a design language and associated software package for developing and experimenting with dataflow-based design techniques for signal processing systems, we have developed a novel tool for functional simulation of PSDF specifications. This simulation tool allows designers to model applications in PSDF and simulate their functionality, including use of the dynamic parameter reconfiguration capabilities offered by PSDF. With the help of this simulation tool, our design methodology helps to map PSDF specifications into efficient implementations on field programmable gate arrays (FPGAs). Furthermore, valid schedules can be derived from the PSDF models at runtime to adapt hardware configurations based on changing data characteristics or operational requirements. Under certain conditions, efficient quasi-static schedules can be applied to reduce overhead and enhance predictability in the scheduling process. Motivated by the fact that scheduling is critical to performance and to efficient use of dynamic reconfiguration, we have focused on a methodology for schedule design, which complements the emphasis on automated schedule construction in the existing literature on dataflow-based design and implementation. In particular, we have proposed a dataflow-based schedule design framework called the dataflow schedule graph (DSG), which provides a graphical framework for schedule construction based on dataflow semantics, and can also be used as an intermediate representation target for automated schedule generation. Our approach to applying the DSG in this thesis emphasizes schedule construction as a design process rather than an outcome of the synthesis process. Our approach employs dataflow graphs for representing both application models and schedules that are derived from them. By providing a dataflow-integrated framework for unambiguously representing, analyzing, manipulating, and interchanging schedules, the DSG facilitates effective codesign of dataflow-based application models and schedules for execution of these models. As multicore processors are deployed in an increasing variety of embedded image processing systems, effective utilization of resources such as multiprocessor systemon-chip (MPSoC) devices, and effective handling of implementation concerns such as memory management and I/O become critical to developing efficient embedded implementations. However, the diversity and complexity of applications and architectures in embedded image processing systems make the mapping of applications onto MPSoCs difficult. We help to address this challenge through a structured design methodology that is built upon the DSG modeling framework. We refer to this methodology as the DEIPS methodology (DSG-based design and implementation of Embedded Image Processing Systems). The DEIPS methodology provides a unified framework for joint consideration of DSG structures and the application graphs from which they are derived, which allows designers to integrate considerations of parallelization and resource constraints together with the application modeling process. We demonstrate the DEIPS methodology through cases studies on practical embedded image processing systems

Adaptive Multiclient Network-on-Chip Memory Core : Hardware Architecture, Software Abstraction Layer, and Application Exploration

This paper presents the hardware architecture and the software abstraction layer of an adaptive multiclient Network-on-Chip (NoC) memory core. The memory core supports the flexibility of a heterogeneous FPGA-based runtime adaptive multiprocessor system called RAMPSoC. The processing elements, also called clients, can access the memory core via the Network-on-Chip (NoC). The memory core supports a dynamic mapping of an address space for the different clients as well as different data transfer modes, such as variable burst sizes. Therefore, two main limitations of FPGA-based multiprocessor systems, the restricted on-chip memory resources and that usually only one physical channel to an off-chip memory exists, are leveraged. Furthermore, a software abstraction layer is introduced, which hides the complexity of the memory core architecture and which provides an easy to use interface for the application programmer. Finally, the advantages of the novel memory core in terms of performance, flexibility, and user friendliness are shown using a real-world image processing application

Adaptive Multiclient Network-on-Chip Memory Core: Hardware Architecture, Software Abstraction Layer, and Application Exploration

This paper presents the hardware architecture and the software abstraction layer of an adaptive multiclient Network-on-Chip (NoC) memory core. The memory core supports the flexibility of a heterogeneous FPGA-based runtime adaptive multiprocessor system called RAMPSoC. The processing elements, also called clients, can access the memory core via the Network-on-Chip (NoC). The memory core supports a dynamic mapping of an address space for the different clients as well as different data transfer modes, such as variable burst sizes. Therefore, two main limitations of FPGA-based multiprocessor systems, the restricted on-chip memory resources and that usually only one physical channel to an off-chip memory exists, are leveraged. Furthermore, a software abstraction layer is introduced, which hides the complexity of the memory core architecture and which provides an easy to use interface for the application programmer. Finally, the advantages of the novel memory core in terms of performance, flexibility, and user friendliness are shown using a real-world image processing application

Dynamically reconfigurable architecture for embedded computer vision systems

The objective of this research work is to design, develop and implement a new architecture which integrates on the same chip all the processing levels of a complete Computer Vision system, so that the execution is efficient without compromising the power consumption while keeping a reduced cost. For this purpose, an analysis and classification of different mathematical operations and algorithms commonly used in Computer Vision are carried out, as well as a in-depth review of the image processing capabilities of current-generation hardware devices. This permits to determine the requirements and the key aspects for an efficient architecture. A representative set of algorithms is employed as benchmark to evaluate the proposed architecture, which is implemented on an FPGA-based system-on-chip. Finally, the prototype is compared to other related approaches in order to determine its advantages and weaknesses