The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Labros Kontokostas - One of the best experts on this subject based on the ideXlab platform.
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US4720918.pdf
internal, 2017Co-Authors: Labros KontokostasAbstract:tips over the ?rst 40 um are shown by solid shaded bands, and the spread of pro?les of known blades is indicated by the cross-hatched bands. In this speci
Milo M. K. Martin - One of the best experts on this subject based on the ideXlab platform.
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Token tenure and PATCH: A predictive/adaptive token-counting hybrid
ACM Transactions on Architecture and Code Optimization, 2010Co-Authors: Arun Raghavan, Colin Blundell, Milo M. K. MartinAbstract:Traditional coherence protocols present a set of difficult trade-offs: the reliance of snoopy protocols on broadcast and ordered interconnects limits their scalability, while directory protocols incur a performance penalty on sharing misses due to indirection. This work introduces Patch (Predictive/Adaptive Token-Counting Hybrid), a coherence protocol that provides the scalability of directory protocols while opportunistically sending direct requests to reduce sharing latency. Patch extends a standard directory protocol to track tokens and use token-counting rules for enforcing coherence permissions. Token counting allows Patch to support direct requests on an unordered interconnect, while a mechanism called token tenure provides broadcast-free forward progress using the directory protocol's per-block point of ordering at the home along with either timeouts at requesters or explicit race notification messages. Patch makes three main contributions. First, Patch introduces token tenure, which provides broadcast-free forward progress for token-counting protocols. Second, Patch deprioritizes best-effort direct requests to match or exceed the performance of directory protocols without restricting scalability. Finally, Patch provides greater scalability than directory protocols when using inexact encodings of sharers because only processors holding tokens need to acknowledge requests. Overall, Patch is a “one-size-fits-all” coherence protocol that dynamically adapts to work well for small systems, large systems, and anywhere in between.
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MICRO - Token tenure: PATCHing token counting using directory-based cache coherence
2008 41st IEEE ACM International Symposium on Microarchitecture, 2008Co-Authors: Arun Raghavan, Colin Blundell, Milo M. K. MartinAbstract:Traditional coherence protocols present a set of difficult tradeoffs: the reliance of snoopy protocols on broadcast and ordered interconnects limits their scalability, while directory protocols incur a performance penalty on sharing misses due to indirection. This work introduces PATCH (Predictive/Adaptive Token Counting Hybrid), a coherence protocol that provides the scalability of directory protocols while opportunistically sending direct requests to reduce sharing latency. PATCH extends a standard directory protocol to track tokens and use token counting rules for enforcing coherence permissions. Token counting allows PATCH to support direct requests on an unordered interconnect, while a mechanism called token tenure uses local processor timeouts and the directorypsilas per-block point of ordering at the home node to guarantee forward progress without relying on broadcast. PATCH makes three main contributions. First, PATCH introduces token tenure, which provides broadcast-free forward progress for token counting protocols. Second, PATCH deprioritizes best-effort direct requests to match or exceed the performance of directory protocols without restricting scalability. Finally, PATCH provides greater scalability than directory protocols when using inexact encodings of sharers because only processors holding tokens need to acknowledge requests. Overall, PATCH is a ldquoone-size-fits-allrdquo coherence protocol that dynamically adapts to work well for small systems, large systems, and anywhere in between.
Florence Tupin - One of the best experts on this subject based on the ideXlab platform.
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Patch similarity under non Gaussian noise
2016Co-Authors: Charles-alban Deledalle, Florence Tupin, Loic DenisAbstract:Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises.
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How to compare noisy patches? Patch similarity beyond Gaussian noise
International Journal of Computer Vision, 2015Co-Authors: Charles-alban Deledalle, Loic Denis, Florence TupinAbstract:Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches (so-called image-based) compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. Recent progress in natural image modeling also makes intensive use of patch comparison. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and machine learning. By expressing patch (dis)similarity as a detection test under a given noise model, we introduce these criteria with a new one and discuss their properties. We then assess their performance for different tasks: patch discrimination, image denoising, stereo-matching and motion-tracking under gamma and Poisson noises. The proposed criterion based on the generalized likelihood ratio is shown to be both easy to derive and powerful in these diverse applications.
Arun Raghavan - One of the best experts on this subject based on the ideXlab platform.
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Token tenure and PATCH: A predictive/adaptive token-counting hybrid
ACM Transactions on Architecture and Code Optimization, 2010Co-Authors: Arun Raghavan, Colin Blundell, Milo M. K. MartinAbstract:Traditional coherence protocols present a set of difficult trade-offs: the reliance of snoopy protocols on broadcast and ordered interconnects limits their scalability, while directory protocols incur a performance penalty on sharing misses due to indirection. This work introduces Patch (Predictive/Adaptive Token-Counting Hybrid), a coherence protocol that provides the scalability of directory protocols while opportunistically sending direct requests to reduce sharing latency. Patch extends a standard directory protocol to track tokens and use token-counting rules for enforcing coherence permissions. Token counting allows Patch to support direct requests on an unordered interconnect, while a mechanism called token tenure provides broadcast-free forward progress using the directory protocol's per-block point of ordering at the home along with either timeouts at requesters or explicit race notification messages. Patch makes three main contributions. First, Patch introduces token tenure, which provides broadcast-free forward progress for token-counting protocols. Second, Patch deprioritizes best-effort direct requests to match or exceed the performance of directory protocols without restricting scalability. Finally, Patch provides greater scalability than directory protocols when using inexact encodings of sharers because only processors holding tokens need to acknowledge requests. Overall, Patch is a “one-size-fits-all” coherence protocol that dynamically adapts to work well for small systems, large systems, and anywhere in between.
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MICRO - Token tenure: PATCHing token counting using directory-based cache coherence
2008 41st IEEE ACM International Symposium on Microarchitecture, 2008Co-Authors: Arun Raghavan, Colin Blundell, Milo M. K. MartinAbstract:Traditional coherence protocols present a set of difficult tradeoffs: the reliance of snoopy protocols on broadcast and ordered interconnects limits their scalability, while directory protocols incur a performance penalty on sharing misses due to indirection. This work introduces PATCH (Predictive/Adaptive Token Counting Hybrid), a coherence protocol that provides the scalability of directory protocols while opportunistically sending direct requests to reduce sharing latency. PATCH extends a standard directory protocol to track tokens and use token counting rules for enforcing coherence permissions. Token counting allows PATCH to support direct requests on an unordered interconnect, while a mechanism called token tenure uses local processor timeouts and the directorypsilas per-block point of ordering at the home node to guarantee forward progress without relying on broadcast. PATCH makes three main contributions. First, PATCH introduces token tenure, which provides broadcast-free forward progress for token counting protocols. Second, PATCH deprioritizes best-effort direct requests to match or exceed the performance of directory protocols without restricting scalability. Finally, PATCH provides greater scalability than directory protocols when using inexact encodings of sharers because only processors holding tokens need to acknowledge requests. Overall, PATCH is a ldquoone-size-fits-allrdquo coherence protocol that dynamically adapts to work well for small systems, large systems, and anywhere in between.
Ashwin K Iyer - One of the best experts on this subject based on the ideXlab platform.
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dual band microstrip patch antenna using integrated uniplanar metamaterial based ebgs
IEEE Transactions on Antennas and Propagation, 2016Co-Authors: Braden P Smyth, Stuart Barth, Ashwin K IyerAbstract:This paper presents a novel dual-band microstrip patch antenna that employs a metamaterial-based electromagnetic bandgap (MTM-EBG) integrated into its radiating edges to support two distinct operating frequencies. The resulting antenna is compact, uniplanar, completely printable, and via-free. Dispersion engineering of the MTM-EBG unit cell through a rigorous multiconductor transmission-line analysis allows simple, systematic design for two or more arbitrary frequencies. Additionally, a novel approach is taken to employ the same MTM-EBG to impedance-match the antenna to an inset microstrip feed at both operating frequencies. A dual-band MTM-EBG antenna designed to radiate at 2.4 and 5.0 GHz is simulated and tested, and experimental results demonstrate radiation performance comparable to the corresponding conventional patch antennas in excellent agreement with simulations, while also affording some degree of miniaturization at lower frequencies.
