1,335 research outputs found
Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations
Specifying and programming graphical interactions are difficult tasks,
notably because designers have difficulties to express the dynamics of the
interaction. This paper shows how the MDPC architecture improves the usability
of the specification and the implementation of graphical interaction. The
architecture is based on the use of picking views and inverse transforms from
the graphics to the data. With three examples of graphical interaction, we show
how to express them with the architecture, how to implement them, and how this
improves programming usability. Moreover, we show that it enables implementing
graphical interaction without a scene graph. This kind of code prevents from
errors due to cache consistency management
Penelope: The NBTI-aware processor
Transistors consist of lower number of atoms with every technology generation. Such atoms may be displaced due to the stress caused by high temperature, frequency and current, leading to failures. NBTI (negative bias temperature instability) is one of the most important sources of failure affecting transistors. NBTI degrades PMOS transistors whenever the voltage at the gate is negative (logic inputPeer ReviewedPostprint (published version
Low-complexity distributed issue queue
As technology evolves, power density significantly increases and cooling systems become more complex and expensive. The issue logic is one of the processor hotspots and, at the same time, its latency is crucial for the processor performance. We present a low-complexity FP issue logic (MB/spl I.bar/distr) that achieves high performance with small energy requirements. The MB/spl I.bar/distr scheme is based on classifying instructions and dispatching them into a set of queues depending on their data dependences. These instructions are selected for issuing based on an estimation of when their operands will be available, so the conventional wakeup activity is not required. Additionally, the functional units are distributed across the different queues. The energy required by the proposed scheme is substantially lower than that required by a conventional issue design, even if the latter has the ability of waking-up only unready operands. MB/spl I.bar/distr scheme reduces the energy-delay product by 35% and the energy-delay product by 18% with respect to a state-of-the-art approach.Peer ReviewedPostprint (published version
Algorithmic issues in visual object recognition
This thesis is divided into two parts covering two aspects of
research in the area of visual object recognition.
Part I is about human detection in still images. Human
detection is a challenging computer vision task due to the wide
variability in human visual appearances and body poses. In this
part, we present several enhancements to human detection
algorithms. First, we present an extension to the integral
images framework to allow for constant time computation of
non-uniformly weighted summations over rectangular regions
using a bundle of integral images. Such computational element
is commonly used in constructing gradient-based feature
descriptors, which are the most successful in shape-based human
detection. Second, we introduce deformable features as an
alternative to the conventional static features used in
classifiers based on boosted ensembles. Deformable features can
enhance the accuracy of human detection by adapting to pose
changes that can be described as translations of body features.
Third, we present a comprehensive evaluation framework for
cascade-based human detectors. The presented framework
facilitates comparison between cascade-based detection
algorithms, provides a confidence measure for result, and
deploys a practical evaluation scenario.
Part II explores the possibilities of enhancing the speed of
core algorithms used in visual object recognition using the
computing capabilities of Graphics Processing Units (GPUs).
First, we present an implementation of Graph Cut on GPUs, which
achieves up to 4x speedup against compared to a CPU
implementation. The Graph Cut algorithm has many applications
related to visual object recognition such as segmentation and
3D point matching. Second, we present an efficient sparse
approximation of kernel matrices for GPUs that can
significantly speed up kernel based learning algorithms, which
are widely used in object detection and recognition. We present
an implementation of the Affinity Propagation clustering
algorithm based on this representation, which is about 6 times
faster than another GPU implementation based on a conventional
sparse matrix representation
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