Joseph T. Buck

Scheduling dynamic dataflow graphs with bounded memory using the token flow model

The authors build upon research by E. A. Lee (1991) concerning the token flow model, an analytical model for the behavior of dataflow graphs with data-dependent control flow, by analyzing the properties of cycles of the schedule: sequences of actor executions that return the graph to its initial state. Necessary and sufficient conditions are given for the existence of a bounded cyclic schedule as well as sufficient conditions for execution of the graph in bounded memory. The techniques presented apply to a more general class of dataflow graphs than previous methods.<<ETX>>

Software synthesis for DSP using Ptolemy

Ptolemy is an environment for simulation, prototyping, and software synthesis for heterogeneous systems. It uses modern object-oriented software technology (in C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. The objectives of Ptolemy encompass practically all aspects of designing signal processing and communications systems, ranging from algorithms and communication strategies, through simulation, hardware and software design, parallel computing, and generation of real-time prototypes. In this paper we will introduce the software synthesis aspects of the Ptolemy system. The environment presented here is both modular and extensible. Ptolemy allows the user to choose among various single- or multiple-processor schedulers.

Isolated-word speech recognition using multisection vector quantization codebooks

A new approach to isolated-word speech recognition using vector quantization (VQ) is examined. In this approach, words are recognized by means of sequences of VQ codebooks, called multisection codebooks. A separate multisection codebook is designed for each word in the recognition vocabulary by dividing the word into equal-length sections and designing a standard VQ codebook for each section. Unknown words are classified by dividing them into corresponding sections, encoding them with the multisection codebooks, and finding the multisection codebook that yields the smallest average distortion. For speaker-independent recognition of the digits, this approach achieved a recognition accuracy of 98 percent. In addition, the approach achieved greater than 99 percent accuracy for speaker-dependent recognition of the digits with only one distortion computation per input frame per vocabulary word. The approach is described, detailed experimental results are presented and discussed, and computational requirements are analyzed.

Multirate signal processing in Ptolemy

The use of two models of computation, synchronous dataflow (SDF) and dynamic dataflow (DDF), to design and implement signal processing applications with multiple sample rates is discussed. The SDF model is used for synchronous applications. SDF is amenable to compile-time scheduling, and hence is much more efficient at runtime. The design environment, Ptolemy, can simultaneously support multiple models of computation, so SDF and DDF can be combined in a single application. Hence, the implementation will incur the run-time cost of DDF only for those asynchronous portions that absolutely must incur such cost. As an illustration, the authors detail a synchronous application, sample-rate conversion using polyphase filters, and an asynchronous application, timing recovery for an amplitude-shift-keyed signal.<<ETX>>

Ptolemy: a framework for simulating and prototyping heterogeneous systems

Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern object-oriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects of designing signal processing and communications systems, ranging from algorithms and communication strategies, simulation, hardware and software design, parallel computing, and generating real-time prototypes. To accommodate this breadth, Ptoloemy must support a plethora of widely differing design styles. The core of Ptolemy is a set of object-oriented class definitions that makes a few assumptions about the system to be modeled; rather, standard interfaces are provided for generic objects and more specialized, application-specific objects are derived from these. A basic abstraction in Ptolemy is the Domain, which realizes a computational model appropriate for a particular type of subsystem. Current examples of domains include synchronous and dynamic dataflow, discrete-event, and others appropriate for control software and embedded microcontrollers. Domains can be mixed as appropriate to realize and overall system simulation. Some current applications of Ptolemy include networking and transport, call-processing and signaling software, embedded microcontrollers, signal processing (including implementation in real-time), scheduling of parallel digital signal processors, board-level hardware timing simulation, and combinations of these.

A scheduling framework for minimizing memory requirements of multirate DSP systems represented as dataflow graphs

Numerous design environments for signal processing use specification languages with semantics closely related to synchronous dataflow (SDF), a restricted form of dataflow that has proven efficient for describing and compiling multirate signal processing algorithms. An SDF representation allows the compiler freedom to explore different ways to sequence the computations in a program, and to evaluate the associated tradeoffs, such as those involving throughput, code size, and buffering requirements. To guide the scheduling process, compilers may apply some form of clustering, in which multiple computations are grouped together according to different criteria. The authors develop clustering techniques to synthesize minimum code size implementations of SDF programs, and describe techniques to incorporate arbitrary clustering strategies into a minimum code size scheduler.<<ETX>>

Parameter selection for isolated word recognition using vector quantization

The use of vector quantization (VQ) in isolated-word recognition of a 20-word vocabulary is examined. A separate sequence of VQ code books is designed for each word in the recognition vocabulary and input words are classified by performing VQ and finding the sequence of code books that achieve the smallest average distortion. In this paper, critical parameters are noted and the results of parameter studies are presented.

Speech noise reduction by means of multi-signal minimum-cross-entropy spectral analysis

This paper presents results of a new spectrum-analysis method that estimates a number of power spectra when a prior estimate of each is available and new information is obtained in the form of values of the auto-correlation function of their sum. The method applies for instance when one obtains autocorrelation measurements for a signal with independent additive interference, and one has prior estimates of the signal and noise spectra. By incorporating prior estimates for both spectra, the method offers considerable flexibility for tailoring an estimator to the characteristics of a signal or noise. The new method, a generalization of Minimum-Cross-Entropy Spectrum Analysis (MCESA) and Maximum Entropy Spectrum Analysis (MESA), is called Multisignal MCESA. Its theoretical basis is reviewed, and results of experimental tests of an implementation are presented. The test data comprise digitized samples of speech corrupted with helicopter noise and tone interference.