Data flow is a natural paradigm for describing DSP applications for concurrent implementation on parallel hardware. Data flow programs for signal processing are directed graphs where each node represents a function and each arc represents a signal path. Synchronous data flow (SDF) is a special case of data flow (either atomic or large grain) in which the number of data samples produced or consumed by each node on each invocation is specified a priori. Nodes can be scheduled statically (at compile time) onto single or parallel programmable processors so the run-time overhead usually associated with data flow evaporates. Multiple sample rates within the same system are easily and naturally handled. Conditions for correctness of SDF graph are explained and scheduling algorithms are described for homogeneous parallel processors sharing memory. A preliminary SDF software system for automatically generating assembly language code for DSP microcomputers is described. Two new efficiency techniques are introduced, static buffering and an extension to SDF to efficiently implement conditionals.

Synchronous data flow

The first edition of this book has enjoyed a gratifying existence. 1s sued in 1965, it found its intended place as a research reference and as a graduate-Ievel text. Research laboratories and universities reported broad use. Published reviews-some twenty-five in number-were universally kind. Subsequently the book was translated and published in Russian (Svyaz; Moscow, 1968) and Spanish (Gredos, S.A.; Madrid, 1972). Copies of the first edition have been exhausted for several years, but demand for the material continues. At the behest of the publisher, and with the encouragement of numerous colleagues, a second edition was begun in 1970. The aim was to retain the original format, but to expand the content, especially in the areas of digital communications and com puter techniques for speech signal processing. As before, the intended audience is the graduate-Ievel engineer and physicist, but the psycho physicist, phonetician, speech scientist and linguist should find material of interest."

Speech Analysis, Synthesis and Perception

Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of programmer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple processors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.

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Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing

Techniques for analysis and synthesis of speech signals are reviewed with emphasis on vocoders and related devices for more efficient transmission and storage of speech. Selected applications of speech coding methods as sensory aids to the handicapped are described.

Vocoders: Analysis and synthesis of speech

Gabriel is a software system intended to manage the complete development of real-time digital signal processing (DSP) applications, from conception and experimentation to implementation in real-time hardware. It performs non-real-time simulations as well as code synthesis for real-time hardware. It is intended to ease code development for architectures that are not easy targets for conventional compilers, such as multiprocessor systems built with very high-performance microcoded DSPs. The system is designed to be retargetable in two ways. First, it can synthesize code for a variety of multi-DSP architectures where the user specifies the salient features of the architecture. Second, it can target different DSPs. The authors have concentrated on code generation for the Motorola DSP56001, although code generation for the AT&T DSP32 has been demonstrated. At the highest level, an algorithm is described using a hierarchical block diagram. At the lowest level, the user can either simulate the algorithm locally on the workstation, simulate the target architecture running the generated code, or download the code into hardware and run it in real time. Gabriel is capable of handling multiple sample rates, iteration, and recurrences. >

Gabriel: a design environment for DSP

A computer program known as BLODI, which accepts for an input a source program written in the BLODI language, is described. The BLODI source language corresponds closely to an engineer's block diagram of a circuit and is easily learned, even by persons not familiar with computing machines. The input code consists essentially of designating the connectivity of a number of boxes drawn from an alphabet of about 30 types. These types include amplifiers, delay lines, counters, etc., which are familiar to designers of electronic circuits. The principles of the compiler are explained and applications are discussed.

V. A. Vyssotsky

Papers

A block diagram compiler