The Experts below are selected from a list of 155361 Experts worldwide ranked by ideXlab platform

H Meyr - One of the best experts on this subject based on the ideXlab platform.

  • fine grained application source code profiling for asip design
    Design Automation Conference, 2005
    Co-Authors: Kingshuk Karuri, Rainer Leupers, M Al A Faruque, Stefan Kraemer, Gerd Ascheid, H Meyr
    Abstract:

    Current application specific instruction set processor (ASIP) design methodologies are mostly based on iterative architecture exploration that uses Architecture Description Languages (ADLs) and retargetable Software Development Tools. However, for improved design efficiency, additional pre-architecture exploration Tools are required to help narrow-down the huge design space and making coarse-grained instruction set architecture (ISA) decisions before detailed ADL modeling. Extensive application code profiling is the key in such early design stages. Based on a novel code instrumentation technology, we present a micro-profiling approach that fills the current gap between source-level and instruction-level profilers and combines their advantages w.r.t. speed and accuracy. We show how the micro-profiler is embedded into an advanced ASIP design flow and justify its use in a case study to design an MP3 decoder ASIP.

  • architecture exploration for embedded processors with lisa
    2002
    Co-Authors: Andreas Hoffmann, H Meyr, Rainer Leupers
    Abstract:

    Foreword. Preface. 1: Introduction. 1. Processor Categories. 2. Advent of ASIPs in System-on-Chip Design. 3. Organization of this Book. 2: Traditional Asip Design Methodology. 1. Related Work. 2. Motivation of this Work. 3: Processor Models For Asip Design. 1. LISA Language. 2. Model Requirements of Tools. 3. Abstraction Levels. 4. Concluding Remarks. 4: Lisa Processor Design Platform. 1. Hardware Designer Platform. 2. Software Designer Platform. 3. System Integrator Platform. 4. Concluding Remarks. 5: Architecture Exploration. 1. From Specification to Implementation. 2. Architecture Exploration Using LISA. 3. Concluding Remarks. 6: Architecture Implementation. 1. The ICORE Architecture. 2. Architecture Generation from LISA. 3. Case Study. 4. Concluding Remarks. 7: Software Tools For Application Design. 1. Code Generation Tools. 2. Simulation. 3. Debugging. 4. Case Studies. 5. Concluding Remarks. 8: System Integration And Verification. 1. Platform-Based Design. 2. Enabling Platform-Based Design. 3. Software Simulator Integration. 4. Case Study: CoCentric System Studio. 5. Concluding Remarks. 9: Summary And Outlook. 1. Processor Modeling. 2. Architecture Exploration. 3. Software Development Tools. 4. Architecture Implementation. 5. Concluding Remarks. Appendices: Abbreviations.Grammar of the LISA Language. Sample ARM7 LISA Model. The ICORE Architecture. List of Figures. List of Examples. List of Tables. Bibliography. About the Authors.

  • a methodology for the design of application specific instruction set processors asip using the machine description language lisa
    International Conference on Computer Aided Design, 2001
    Co-Authors: Andreas Hoffmann, Achim Nohl, Gunnar Braun, Oliver Schliebusch, O Wahlen, H Meyr
    Abstract:

    The Development of application specific instruction set processors (ASIP) is currently the exclusive domain of the semiconductor houses and core vendors. This is due to the fact that building such an architecture is a difficult task that requires expertise knowledge in different domains: application Software Development Tools, processor hardware implementation, and system integration and verification. This paper presents a retargetable framework for ASIP design which is based on machine descriptions in the LISA language. From that, Software Development Tools can be automatically generated including HLL C-compiler, assembler, linker, simulator and debugger frontend. Moreover, synthesizable HDL code can be derived which can then be processed by standard synthesis Tools. Implementation results for a low-power ASIP for DVB-T acquisition and tracking algorithms designed with the presented methodology will be given.

  • a novel methodology for the design of application specific instruction set processors asips using a machine description language
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2001
    Co-Authors: Andreas Hoffmann, Achim Nohl, Gunnar Braun, Tim Kogel, Oliver Schliebusch, O Wahlen, Andreas Wieferink, H Meyr
    Abstract:

    The Development of application-specific instruction-set processors (ASIP) is currently the exclusive domain of the semiconductor houses and core vendors. This is due to the fact that building such an architecture is a difficult task that requires expertise in different domains: application Software Development Tools, processor hardware implementation, and system integration and verification. This paper presents a retargetable framework for ASIP design which is based on machine descriptions in the LISA language. From that, Software Development Tools can be generated automatically including high-level language C compiler, assembler, linker, simulator, and debugger frontend. Moreover, for architecture implementation, synthesizable hardware description language code can be derived, which can then be processed by standard synthesis Tools. Implementation results for a low-power ASIP for digital video broadcasting terrestrial acquisition and tracking algorithms designed with the presented methodology are given. To show the quality of the generated Software Development Tools, they are compared in speed and functionality with commercially available Tools of state-of-the-art digital signal processor and /spl mu/C architectures.

  • generating production quality Software Development Tools using a machine description language
    Design Automation and Test in Europe, 2001
    Co-Authors: Andreas Hoffmann, Achim Nohl, Stefan Pees, Gunnar Braun, H Meyr
    Abstract:

    This paper presents a methodology to automatically generate production quality Software Development Tools for programmable architectures using the machine description language LISA. Various architectures presenting diverse architectural originalities will be presented and the feasibility of automatically generating simulator, assembler, linker and graphical debugger frontend are discussed. The presented approach is not limited to a fixed abstraction level-case studies of the Texas Instruments C62x and C54x, the Analog Devices ADSP2101 as well as the ARM7 show the applicability of the methodology from cycle/phase to instruction accurate models.

Andrea Janes - One of the best experts on this subject based on the ideXlab platform.

  • big code big vocabulary open vocabulary models for source code
    International Conference on Software Engineering, 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work; and 3) showing that such models outperform the state of the art on three distinct code corpora (Java, C, Python). To our knowledge, these are the largest NLMs for code that have been reported. All datasets, code, and trained models used in this work are publicly available.

  • open vocabulary models for source code
    International Conference on Software Engineering, 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.

  • Open-Vocabulary Models for Source Code (Extended Abstract)
    2020 IEEE ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale.In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.

Rafael-michael Karampatsis - One of the best experts on this subject based on the ideXlab platform.

  • big code big vocabulary open vocabulary models for source code
    International Conference on Software Engineering, 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work; and 3) showing that such models outperform the state of the art on three distinct code corpora (Java, C, Python). To our knowledge, these are the largest NLMs for code that have been reported. All datasets, code, and trained models used in this work are publicly available.

  • open vocabulary models for source code
    International Conference on Software Engineering, 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.

  • Open-Vocabulary Models for Source Code (Extended Abstract)
    2020 IEEE ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale.In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.

  • maybe deep neural networks are the best choice for modeling source code
    arXiv: Software Engineering, 2019
    Co-Authors: Rafael-michael Karampatsis, Charles Sutton
    Abstract:

    Statistical language modeling techniques have successfully been applied to source code, yielding a variety of new Software Development Tools, such as Tools for code suggestion and improving readability. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. But traditional language models limit the vocabulary to a fixed set of common words. For code, this strong assumption has been shown to have a significant negative effect on predictive performance. But the open vocabulary version of the neural network language models for code have not been introduced in the literature. We present a new open-vocabulary neural language model for code that is not limited to a fixed vocabulary of identifier names. We employ a segmentation into subword units, subsequences of tokens chosen based on a compression criterion, following previous work in machine translation. Our network achieves best in class performance, outperforming even the state-of-the-art methods of Hellendoorn and Devanbu that are designed specifically to model code. Furthermore, we present a simple method for dynamically adapting the model to a new test project, resulting in increased performance. We showcase our methodology on code corpora in three different languages of over a billion tokens each, hundreds of times larger than in previous work. To our knowledge, this is the largest neural language model for code that has been reported.

Janusz Zalewski - One of the best experts on this subject based on the ideXlab platform.

  • Experimental evaluation of Software Development Tools for safety-critical real-time systems
    Innovations in Systems and Software Engineering, 2005
    Co-Authors: Andrew J. Kornecki, Janusz Zalewski
    Abstract:

    Since the early years of computing, programmers, systems analysts, and Software engineers have sought ways to improve Development process efficiency. Software Development Tools are programs that help developers create other programs and automate mundane operations while bringing the level of abstraction closer to the application engineer. In practice, Software Development Tools have been in wide use among safety-critical system developers. Typical application areas include space, aviation, automotive, nuclear, railroad, medical, and military. While their use is widespread in safety-critical systems, the Tools do not always assure the safe behavior of their respective products. This study examines the assumptions, practices, and criteria for assessing Software Development Tools for building safety-critical real-time systems. Experiments were designed for an avionics testbed and conducted on six industry-strength Tools to assess their functionality, usability, efficiency, and traceability. The results some light on possible improvements in the tool evaluation process that can lead to potential tool qualification for safety-critical real-time systems.

  • assessment of Software Development Tools for safety critical real time systems
    IFAC Proceedings Volumes, 2003
    Co-Authors: Andrew J. Kornecki, Janusz Zalewski
    Abstract:

    Abstract The paper presents guidelines on criteria and procedures for evaluating Software Development Tools used in safety-critical real-time systems. We present, first, a view of the taxonomy of Software Development Tools from the perspective of the Development process and the Development environment. The investigation concentrates on evaluating the design Tools, considering their interfaces with the requirements and testing phases of the Software lifecycle. Furthermore, we discuss the taxonomy of criteria for tool evaluation. The major observations are related to the differences between evaluating the tool itself (macro-evaluation), evaluating the process of producing the tool (meta-evaluation), and evaluating products developed with this tool (micro-evaluation). Building the criteria for evaluation is based on the triad: choosing an appropriate attribute (property) of the tool, describing the metric for the evaluation of this property, and applying some measure (measurement procedure) to use the metric and obtain the results of evaluation of this property.

Charles Sutton - One of the best experts on this subject based on the ideXlab platform.

  • big code big vocabulary open vocabulary models for source code
    International Conference on Software Engineering, 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work; and 3) showing that such models outperform the state of the art on three distinct code corpora (Java, C, Python). To our knowledge, these are the largest NLMs for code that have been reported. All datasets, code, and trained models used in this work are publicly available.

  • open vocabulary models for source code
    International Conference on Software Engineering, 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale. In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.

  • Open-Vocabulary Models for Source Code (Extended Abstract)
    2020 IEEE ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2020
    Co-Authors: Rafael-michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
    Abstract:

    Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new Software Development Tools, such as Tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale.In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.

  • maybe deep neural networks are the best choice for modeling source code
    arXiv: Software Engineering, 2019
    Co-Authors: Rafael-michael Karampatsis, Charles Sutton
    Abstract:

    Statistical language modeling techniques have successfully been applied to source code, yielding a variety of new Software Development Tools, such as Tools for code suggestion and improving readability. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. But traditional language models limit the vocabulary to a fixed set of common words. For code, this strong assumption has been shown to have a significant negative effect on predictive performance. But the open vocabulary version of the neural network language models for code have not been introduced in the literature. We present a new open-vocabulary neural language model for code that is not limited to a fixed vocabulary of identifier names. We employ a segmentation into subword units, subsequences of tokens chosen based on a compression criterion, following previous work in machine translation. Our network achieves best in class performance, outperforming even the state-of-the-art methods of Hellendoorn and Devanbu that are designed specifically to model code. Furthermore, we present a simple method for dynamically adapting the model to a new test project, resulting in increased performance. We showcase our methodology on code corpora in three different languages of over a billion tokens each, hundreds of times larger than in previous work. To our knowledge, this is the largest neural language model for code that has been reported.