Praveen K. Murthy – författare

Software Synthesis from Dataflow Graphs addresses the problem of generating efficient software implementations from applications specified as synchronous dataflow graphs for programmable digital signal processors (DSPs) used in embedded real- time systems. The advent of high-speed graphics workstations has made feasible the use of graphical block diagram programming environments by designers of signal processing systems. A particular subset of dataflow, called Synchronous Dataflow (SDF), has proven efficient for representing a wide class of unirate and multirate signal processing algorithms, and has been used as the basis for numerous DSP block diagram-based programming environments such as the Signal Processing Workstation from Cadence Design Systems, Inc., COSSAP from Synopsys® (both commercial tools), and the Ptolemy environment from the University of California at Berkeley. A key property of the SDF model is that static schedules can be determined at compile time. This removes the overhead of dynamic scheduling and is thus useful for real-time DSP programs where throughput requirements are often severe. Another constraint that programmable DSPs for embedded systems have is the limited amount of on-chip memory. Off-chip memory is not only expensive but is also slower and increases the power consumption of the system; hence, it is imperative that programs fit in the on-chip memory whenever possible. Software Synthesis from Dataflow Graphs reviews the state-of-the-art in constructing static, memory-optimal schedules for programs expressed as SDF graphs. Code size reduction is obtained by the careful organization of loops in the target code. Data buffering is optimized by constructing the loop hierarchy in provably optimal ways for many classes of SDF graphs. The central result is a uniprocessor scheduling framework that provably synthesizes the most compact looping structures, called singleappearance schedules, for a certain class of SDF graphs. In addition, algorithms and heuristics are presented that generate single appearance schedules optimized for data buffering usage. Numerous practical examples and extensive experimental data are provided to illustrate the efficacy of these techniques.

Although programming in memory-restricted environments is never easy, this holds especially true for digital signal processing (DSP). The data-rich, computation-intensive nature of DSP makes memory management a chief and challenging concern for designers. Memory Management for Synthesis of DSP Software focuses on minimizing memory requirements during the synthesis of DSP software from dataflow representations. Dataflow representations are used in many popular DSP design tools, and the methods of this book can be applied in that context, as well as other contexts where dataflow is used.This book systematically reviews research conducted by the authors on memory minimization techniques for compiling synchronous dataflow (SDF) specifications. Beginning with an overview of the foundations of software synthesis techniques from SDF descriptions, it examines aggressive buffer-sharing techniques that take advantage of specific and quantifiable tradeoffs between code size and buffer size to achieve high levels of buffer memory optimization.The authors outline coarse-level strategies using lifetime analysis and dynamic storage allocation (DSA) for efficient buffer sharing as one approach and demonstrate the role of the CBP (consumed-before-produced) parameter at a finer level using a merging framework for buffer sharing. They present two powerful algorithms for combining these sharing techniques and then introduce techniques that are not restricted to the single appearance scheduling space of the other techniques.Extensively illustrated to clarify the mathematical concepts, Memory Management for Synthesis of DSP Software presents a comprehensive survey of state-of-the-art research in DSP software synthesis.