68,137 research outputs found
A Golden Age of Hardware Description Languages: Applying Programming Language Techniques to Improve Design Productivity
Leading experts have declared that there is an impending golden age of computer architecture. During this age, the rate at which architects will be able to innovate will be directly tied to the design and implementation of the hardware description languages they use. Thus, the programming languages community stands on the critical path to this new golden age. This implies that we are also on the cusp of a golden age of hardware description languages. In this paper, we discuss the intellectual challenges facing researchers interested in hardware description language design, compilers, and formal methods. The major theme will be identifying opportunities to apply programming language techniques to address issues in hardware design productivity. Then, we present a vision for a multi-language system that provides a framework for developing solutions to these intellectual problems. This vision is based on a meta-programmed host language combined with a core embedded hardware description language that is used as the basis for the research and development of a sea of domain-specific languages. Central to the design of this system is the core language which is based on an abstraction that provides a general mechanism for the composition of hardware components described in any language
A Hierachical Infrastrucutre for SOC Test Management
HD2BIST - a complete hierarchical framework for BIST scheduling, data-patterns delivery, and diagnosis of complex systems - maximizes and simplifies the reuse of built-in test architectures. HD2BIST optimizes the flexibility for chip designers in planning an overall SoC test strategy by defining a test access method that provides direct virtual access to each core of the system
Learning Fine-grained Image Similarity with Deep Ranking
Learning fine-grained image similarity is a challenging task. It needs to
capture between-class and within-class image differences. This paper proposes a
deep ranking model that employs deep learning techniques to learn similarity
metric directly from images.It has higher learning capability than models based
on hand-crafted features. A novel multiscale network structure has been
developed to describe the images effectively. An efficient triplet sampling
algorithm is proposed to learn the model with distributed asynchronized
stochastic gradient. Extensive experiments show that the proposed algorithm
outperforms models based on hand-crafted visual features and deep
classification models.Comment: CVPR 201
Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI)
images could help in diagnosis and treatment of kidney diseases of children.
Automatic segmentation of renal parenchyma is an important step in this
process. In this paper, we propose a time and memory efficient fully automated
segmentation method which achieves high segmentation accuracy with running time
in the order of seconds in both normal kidneys and kidneys with hydronephrosis.
The proposed method is based on a cascaded application of two 3D convolutional
neural networks that employs spatial and temporal information at the same time
in order to learn the tasks of localization and segmentation of kidneys,
respectively. Segmentation performance is evaluated on both normal and abnormal
kidneys with varying levels of hydronephrosis. We achieved a mean dice
coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric
patients, respectively
Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments
In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system
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