Gabriel: a design environment for DSP

Lee, Edward A.; Ho, W.-H.; Goei, E.E.; Bier, Jeff; Bhattacharyya, Shuvra S.

doi:10.1109/29.46557

Cited by 78 publications

(17 citation statements)

References 15 publications

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“…Such a Scheduler will not always work with DDF Stars. SDF is an appropriate model for multirate signal processing systems with rationally-related sampling rates throughout [3], and is the model used exclusively in Ptolemy's predecessor system Gabriel [19] [2]. The advantages of SDF are ease of programming (since the availability of data tokens is static and doesn't need to be checked), a greater degree of setup-time syntax checking (since sample-rate inconsistencies are easily detected by the system), run-time efficiency (since the ordering of Block invocation is statically determined at setup-time rather dynamically at run-time), and automatic parallel scheduling [29][30] [20].…”

Section: Heterogeneity: the Domainmentioning

confidence: 99%

Ptolemy: A Framework for Simulating and Prototyping Heterogeneous Systems

Buck

Lee

et al. 2002

Readings in Hardware/Software Co-Design

489

328

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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, Ptolemy must support a plethora of widely-differing design styles. The core of Ptolemy is a set of object-oriented class definitions that makes 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 an overall system simulation. Some current applications of Ptolemy include networking and transport, call-processing and signaling software, embedded microcontrollers, signal processing (including implementation in realtime real-time), scheduling of parallel digital signal processors, board-level hardware timing simulation, and combinations of these.Key words: Heterogeneity, mixed-mode, simulation, prototyping, object-oriented programming INTRODUCTIONPtolemy is a simulation and rapid prototyping framework for heterogeneous systems. It is ideal for applications in which heterogeneity is a key characteristic. such as:• Design of multimedia networks;• Real-time embedded software;• Hardware/Software codesign;• Control and call-processing in telecommunications networks;• Rapid prototyping of new telecommunications services;• Mixed-mode hardware simulation;• Mapping applications onto heterogeneous multiprocessor systems; and• Mixed signal processing and real-time control.For example, in the design of multimedia networks, we are interested in studying the interaction between transport, compression or compositing signal processing, and control software in voice or video services over cell-relay networks. In telecommunication systems we are interested in studying the interaction between call-processing software and hardware switch elements. To develop new telecommunication services, we must jointly design control software, signal processing, transport, and hardware elements.In hardware design we are interested in modeling components with varying detail, such as behavioral, logic, timing, and circuit. In system-level design, we may wish to jointly design the communications infrastructure and the processing elements. We may also wish to synthesize (in combination) microcode for specialized processors, C code for generic processors, routi...

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Section: Heterogeneity: the Domainmentioning

confidence: 99%

Ptolemy: A Framework for Simulating and Prototyping Heterogeneous Systems

Buck

Lee

et al. 2002

Readings in Hardware/Software Co-Design

489

328

View full text Add to dashboard Cite

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“…An SST has an associated parameter set K. Nodes within the schedule tree can be parameterized in terms of this parameter set (we will describe this in more detail below). The semantics of how SST parameters (i.e., values associated with elements of K) change is not specified in the SST model; rather, it is determined by the model of computation that is used for application specification (e.g., SDF with static graph parameters [25], parameterized dataflow [14], or scenario aware dataflow [26]), in conjunction with the scheduling strategy that is used to derive the schedule tree. This decoupling from parameter change semantics allows the SST model to be applied to a variety of different kinds of dataflow application models and design environments.…”

mentioning

confidence: 99%

Design and Synthesis for Multimedia Systems Using the Targeted Dataflow Interchange Format

Shen

Sane

et al. 2012

IEEE Trans. Multimedia

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Development of multimedia systems that can be targeted to different platforms is challenging due to the need for rigorous integration between high level abstract modeling, and low level synthesis and optimization. In this paper, a new dataflow-based design tool called the targeted dataflow interchange format (TDIF) is introduced for retargetable design, analysis, and implementation of embedded software for multimedia systems.Our approach provides novel capabilities, based on principles of task-level dataflow analysis, for exploring and optimizing interactions across design components; object-oriented data structures for encapsulating contextual information for components; a novel model for representing parameterized schedules that are derived from repetitive graph structures; and automated code generation for programming interfaces and low level customizations that are geared toward high performance embedded processing architectures. We demonstrate our design tool for cross-platform application design, parameterized schedule representation, and associated dataflow graph code generation using a case study centered around an image registration application.

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“…Commercial tools that employ SDF semantics include Simulink by The Math Works, SPW by Cadence, and ADS by Hewlett Packard. SDF-based research tools include Gabriel [32] and several key domains in Ptolemy [16], from U.C. Berkeley; and ASSIGN from Carnegie Mellon [40].…”

Section: Examplementioning

confidence: 99%

Hardware/Software Cosynthesis of DSP Systems

Bhattacharyya¹

2001

Signal Processing and Communications

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This chapter focuses on the automated mapping of high level specifications of DSP applications into implementation platforms that employ programmable DSPs. Since programmable DSPs are often used in conjunction with other types of programmable processors, such as microcontrollers and general-purpose microprocessors, and with various types of hardware modules, such as FPGAs, and ASIC circuitry, this mapping task, in general, is one of cosynthesis -the joint synthesis of both hardware and software -for a heterogeneous multiprocessor.Since a large variety of cosynthesis techniques have been developed to date, it is not possible here to provide comprehensive coverage of the field. Instead, we focus on a subset of topics that are central to DSP-oriented cosynthesis -application modeling, hardware/software partitioning, synchronization optimization, and block-processing. Some important topics related to cosynthesis that are not covered here include memory management [1,11,26,37,53], which is discussed in Chapter 10; DSP code generation from procedural language specifications [39], which is the topic of Chapter 7; and performance analysis [36,49,54].Additionally, we focus on synthesis from coarse-grain dataflow models due to the increasing importance of such modeling in DSP design tools, and the ability of such modeling to expose valuable, high-level structure of DSP applications that is difficult to deduce from within compilers for general purpose programming models, and other types of models. Thus, we do not explore techniques for fine-grain cosynthesis [21], including synthesis of applicationspecific instruction processors (ASIPs) [43], nor do we explore cosynthesis for control-dominant systems, such as those based on procedural language specifications [22], communicating sequential processes [50], and finite state machine models [6]. All of these are important directions within cosynthesis research, but they do not fit centrally within the DSP-oriented scope of this chapter.

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Gabriel: a design environment for DSP

Cited by 78 publications

References 15 publications

Ptolemy: A Framework for Simulating and Prototyping Heterogeneous Systems

Ptolemy: A Framework for Simulating and Prototyping Heterogeneous Systems

Design and Synthesis for Multimedia Systems Using the Targeted Dataflow Interchange Format

Hardware/Software Cosynthesis of DSP Systems

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