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

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

SCALABLE TECHNIQUES FOR SCHEDULING AND MAPPING DSP APPLICATIONS ONTO EMBEDDED MULTIPROCESSOR PLATFORMS

A variety of multiprocessor architectures has proliferated even for off-the-shelf computing platforms. To make use of these platforms, traditional implementation frameworks focus on implementing Digital Signal Processing (DSP) applications using special platform features to achieve high performance. However, due to the fast evolution of the underlying architectures, solution redevelopment is error prone and re-usability of existing solutions and libraries is limited. In this thesis, we facilitate an efficient migration of DSP systems to multiprocessor platforms while systematically leveraging previous investment in optimized library kernels using dataflow design frameworks. We make these library elements, which are typically tailored to specialized architectures, more amenable to extensive analysis and optimization using an efficient and systematic process. In this thesis we provide techniques to allow such migration through four basic contributions: 1. We propose and develop a framework to explore efficient utilization of Single Instruction Multiple Data (SIMD) cores and accelerators available in heterogeneous multiprocessor platforms consisting of General Purpose Processors (GPPs) and Graphics Processing Units (GPUs). We also propose new scheduling techniques by applying extensive block processing in conjunction with appropriate task mapping and task ordering methods that match efficiently with the underlying architecture. The approach gives the developer the ability to prototype a GPU-accelerated application and explore its design space efficiently and effectively. 2. We introduce the concept of Partial Expansion Graphs (PEGs) as an implementation model and associated class of scheduling strategies. PEGs are designed to help realize DSP systems in terms of forms and granularities of parallelism that are well matched to the given applications and targeted platforms. PEGs also facilitate derivation of both static and dynamic scheduling techniques, depending on the amount of variability in task execution times and other operating conditions. We show how to implement efficient PEG-based scheduling methods using real time operating systems, and to re-use pre-optimized libraries of DSP components within such implementations. 3. We develop new algorithms for scheduling and mapping systems implemented using PEGs. Collectively, these algorithms operate in three steps. First, the amount of data parallelism in the application graph is tuned systematically over many iterations to profit from the available cores in the target platform. Then a mapping algorithm that uses graph analysis is developed to distribute data and task parallel instances over different cores while trying to balance the load of all processing units to make use of pipeline parallelism. Finally, we use a novel technique for performance evaluation by implementing the scheduler and a customizable solution on the programmable platform. This allows accurate fitness functions to be measured and used to drive runtime adaptation of schedules. 4. In addition to providing scheduling techniques for the mentioned applications and platforms, we also show how to integrate the resulting solution in the underlying environment. This is achieved by leveraging existing libraries and applying the GPP-GPU scheduling framework to augment a popular existing Software Defined Radio (SDR) development environment -- GNU Radio -- with a dataflow foundation and a stand-alone GPU-accelerated library. We also show how to realize the PEG model on real time operating system libraries, such as the Texas Instruments DSP/BIOS. A code generator that accepts a manual system designer solution as well as automatically configured solutions is provided to complete the design flow starting from application model to running system

THE RAFAEL MULTI-TARGET HETEROGENEOUS SIGNAL-FLOW GRAPH COMPILER

This paper describes a signal-flow graph compiler which produces distributed code for heterogeneous target systems. The compiler is devoted for mainly Digital Signal Process- ing problems. The code generator features reprogrammable operation library, the static scheduler supports fully heterogeneous systems and the input graph may contain run-time decisions in a limited way. The system has been implemented on IBM PC compatibles under MS-VVindows so it does not require expansive host computer

Algorithms and VLSI architectures for parametric additive synthesis

A parametric additive synthesis approach to sound synthesis is advantageous as it can model sounds in a large scale manner, unlike the classical sinusoidal additive based synthesis paradigms. It is known that a large body of naturally occurring sounds are resonant in character and thus fit the concept well. This thesis is concerned with the computational optimisation of a super class of form ant synthesis which extends the sinusoidal parameters with a spread parameter known as band width. Here a modified formant algorithm is introduced which can be traced back to work done at IRCAM, Paris. When impulse driven, a filter based approach to modelling a formant limits the computational work-load. It is assumed that the filter's coefficients are fixed at initialisation, thus avoiding interpolation which can cause the filter to become chaotic. A filter which is more complex than a second order section is required. Temporal resolution of an impulse generator is achieved by using a two stage polyphase decimator which drives many filterbanks. Each filterbank describes one formant and is composed of sub-elements which allow variation of the formant’s parameters. A resource manager is discussed to overcome the possibility of all sub- banks operating in unison. All filterbanks for one voice are connected in series to the impulse generator and their outputs are summed and scaled accordingly. An explorative study of number systems for DSP algorithms and their architectures is investigated. I invented a new theoretical mechanism for multi-level logic based DSP. Its aims are to reduce the number of transistors and to increase their functionality. A review of synthesis algorithms and VLSI architectures are discussed in a case study between a filter based bit-serial and a CORDIC based sinusoidal generator. They are both of similar size, but the latter is always guaranteed to be stable

Integrated Software Synthesis for Signal Processing Applications

Signal processing applications usually encounter multi-dimensional real-time performance requirements and restrictions on resources, which makes software implementation complex. Although major advances have been made in embedded processor technology for this application domain -- in particular, in technology for programmable digital signal processors -- traditional compiler techniques applied to such platforms do not generate machine code of desired quality. As a result, low-level, human-driven fine tuning of software implementations is needed, and we are therefore in need of more effective strategies for software implementation for signal processing applications. In this thesis, a number of important memory and performance optimization problems are addressed for translating high-level representations of signal processing applications into embedded software implementations. This investigation centers around signal processing-oriented dataflow models of computation. This form of dataflow provides a coarse grained modeling approach that is well-suited to the signal processing domain and is increasingly supported by commercial and research-oriented tools for design and implementation of signal processing systems. Well-developed dataflow models of signal processing systems expose high-level application structure that can be used by designers and design tools to guide optimization of hardware and software implementations. This thesis advances the suite of techniques available for optimization of software implementations that are derived from the application structure exposed from dataflow representations. In addition, the specialized architecture of programmable digital signal processors is considered jointly with dataflow-based analysis to streamline the optimization process for this important family of embedded processors. The specialized features of programmable digital signal processors that are addressed in this thesis include parallel memory banks to facilitate data parallelism, and signal-processing-oriented addressing modes and address register management capabilities. The problems addressed in this thesis involve several inter-related features, and therefore an integrated approach is required to solve them effectively. This thesis proposes such an integrated approach, and develops the approach through formal problem formulations, in-depth theoretical analysis, and extensive experimentation