2,985 research outputs found
Just In Time Assembly (JITA) - A Run Time Interpretation Approach for Achieving Productivity of Creating Custom Accelerators in FPGAs
The reconfigurable computing community has yet to be successful in allowing programmers to access FPGAs through traditional software development flows. Existing barriers that prevent programmers from using FPGAs include: 1) knowledge of hardware programming models, 2) the need to work within the vendor specific CAD tools and hardware synthesis. This thesis presents a series of published papers that explore different aspects of a new approach being developed to remove the barriers and enable programmers to compile accelerators on next generation reconfigurable manycore architectures. The approach is entitled Just In Time Assembly (JITA) of hardware accelerators. The approach has been defined to allow hardware accelerators to be built and run through software compilation and run time interpretation outside of CAD tools and without requiring each new accelerator to be synthesized. The approach advocates the use of libraries of pre-synthesized components that can be referenced through symbolic links in a similar fashion to dynamically linked software libraries. Synthesis still must occur but is moved out of the application programmers software flow and into the initial coding process that occurs when programming patterns that define a Domain Specific Language (DSL) are first coded. Programmers see no difference between creating software or hardware functionality when using the DSL. A new run time interpreter is introduced to assemble the individual pre-synthesized hardware accelerators that comprise the accelerator functionality within a configurable tile array of partially reconfigurable slots at run time. Quantitative results are presented that compares utilization, performance, and productivity of the approach to what would be achieved by full custom accelerators created through traditional CAD flows using hardware programming models and passing through synthesis
Techniques for the Synthesis of Reversible Toffoli Networks
This paper presents novel techniques for the synthesis of reversible networks
of Toffoli gates, as well as improvements to previous methods. Gate count and
technology oriented cost metrics are used. Our synthesis techniques are
independent of the cost metrics. Two new iterative synthesis procedure
employing Reed-Muller spectra are introduced and shown to complement earlier
synthesis approaches. The template simplification suggested in earlier work is
enhanced through introduction of a faster and more efficient template
application algorithm, updated (shorter) classification of the templates, and
presentation of the new templates of sizes 7 and 9. A novel ``resynthesis''
approach is introduced wherein a sequence of gates is chosen from a network,
and the reversible specification it realizes is resynthesized as an independent
problem in hopes of reducing the network cost. Empirical results are presented
to show that the methods are effective both in terms of the realization of all
3x3 reversible functions and larger reversible benchmark specifications.Comment: 20 pages, 5 figure
Exploring Medical Breakthroughs: A Systematic Review of ChatGPT Applications in Healthcare
ChatGPT, a large language model developed by OpenAI, has emerged as a powerful tool in the field of medicine. In this systematic review, we explore the potential of ChatGPT in various medical applications by analyzing articles related to medicine and healthcare. We carefully examined the methodologies, results, and conclusions of these articles to provide a comprehensive overview of the current evidence on the use of ChatGPT in the field of medicine. Through this review, we highlight how ChatGPT has been utilized to streamline and simplify complex tasks, improve patient care, enhance clinical decision-making, and facilitate communication among healthcare professionals. We also discuss the challenges and limitations of using ChatGPT in medicine, including concerns related to privacy, ethical considerations, and potential biases. Despite these challenges, ChatGPT has shown great promise in transforming the landscape of medicine and has the potential to revolutionize healthcare delivery. By synthesizing the findings from these articles, we aim to provide a critical and evidence-based evaluation of the current state of ChatGPT in medicine, and to identify areas for further research and development
Automated Fixing of Programs with Contracts
This paper describes AutoFix, an automatic debugging technique that can fix
faults in general-purpose software. To provide high-quality fix suggestions and
to enable automation of the whole debugging process, AutoFix relies on the
presence of simple specification elements in the form of contracts (such as
pre- and postconditions). Using contracts enhances the precision of dynamic
analysis techniques for fault detection and localization, and for validating
fixes. The only required user input to the AutoFix supporting tool is then a
faulty program annotated with contracts; the tool produces a collection of
validated fixes for the fault ranked according to an estimate of their
suitability.
In an extensive experimental evaluation, we applied AutoFix to over 200
faults in four code bases of different maturity and quality (of implementation
and of contracts). AutoFix successfully fixed 42% of the faults, producing, in
the majority of cases, corrections of quality comparable to those competent
programmers would write; the used computational resources were modest, with an
average time per fix below 20 minutes on commodity hardware. These figures
compare favorably to the state of the art in automated program fixing, and
demonstrate that the AutoFix approach is successfully applicable to reduce the
debugging burden in real-world scenarios.Comment: Minor changes after proofreadin
Methods and Applications of Synthetic Data Generation
The advent of data mining and machine learning has highlighted the value of large and varied sources of data, while increasing the demand for synthetic data captures the structural and statistical characteristics of the original data without revealing personal or proprietary information contained in the original dataset.
In this dissertation, we use examples from original research to show that, using appropriate models and input parameters, synthetic data that mimics the characteristics of real data can be generated with sufficient rate and quality to address the volume, structural complexity, and statistical variation requirements of research and development of digital information processing systems.
First, we present a progression of research studies using a variety of tools to generate synthetic network traffic patterns, enabling us to observe relationships between network latency and communication pattern benchmarks at all levels of the network stack.
We then present a framework for synthesizing large scale IoT data with complex structural characteristics in a scalable extraction and synthesis framework, and demonstrate the use of generated data in the benchmarking of IoT middleware.
Finally, we detail research on synthetic image generation for deep learning models using 3D modeling. We find that synthetic images can be an effective technique for augmenting limited sets of real training data, and in use cases that benefit from incremental training or model specialization, we find that pretraining on synthetic images provided a usable base model for transfer learning
Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design
The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface
RADIC Voice Authentication: Replay Attack Detection using Image Classification for Voice Authentication Systems
Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks.
This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay attacks
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