scispace - formally typeset
Search or ask a question
Institution

Intel

CompanySanta Clara, California, United States
About: Intel is a company organization based out in Santa Clara, California, United States. It is known for research contribution in the topics: Cache & Signal. The organization has 41727 authors who have published 68805 publications receiving 1637184 citations. The organization is also known as: Intel Corporation & N M Electronics.


Papers
More filters
Journal ArticleDOI
19 Oct 2003
TL;DR: Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality, considerably outperform competing commercial and freely available solutions.
Abstract: Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

6,326 citations

Proceedings Article
30 Apr 2016
TL;DR: This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems.
Abstract: State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.

5,566 citations

Proceedings ArticleDOI
28 Sep 2002
TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Abstract: We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring in general, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The application-driven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and health monitoring.

4,623 citations

Journal ArticleDOI
12 Jun 2005
TL;DR: The goals are to provide easy-to-use, portable, transparent, and efficient instrumentation, and to illustrate Pin's versatility, two Pintools in daily use to analyze production software are described.
Abstract: Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy-to-use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C++ using Pin's rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture-specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application's original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re-allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic-block counting. To illustrate Pin's versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32-bit x86), EM64T (64-bit x86), Itanium®, and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.

4,019 citations

Posted Content
TL;DR: In this article, a new convolutional network module is proposed to aggregate multi-scale contextual information without losing resolution, and the architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage.
Abstract: State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.

4,018 citations


Authors

Showing all 41784 results

NameH-indexPapersCitations
David R. Williams1782034138789
Pete Smith1562464138819
Kevin Murphy146728120475
Yu Huang136149289209
Robert W. Heath128104973171
Don Towsley11988356671
Bo Wang119290584863
Michael Gardner11775359734
David E. Culler11642976131
Jack Dongarra113131565498
Sanjeev Kumar113132554386
John A. Stankovic10955951329
Sofia Vallecorsa10856951786
Dieter Fox10834053157
Ching-Ping Wong106112842835
Network Information
Related Institutions (5)
Hewlett-Packard
59.8K papers, 1.4M citations

94% related

IBM
253.9K papers, 7.4M citations

92% related

Samsung
163.6K papers, 2M citations

92% related

Georgia Institute of Technology
119K papers, 4.6M citations

88% related

Microsoft
86.9K papers, 4.1M citations

87% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202316
202265
20211,167
20202,426
20193,948
20182,937