152,264 research outputs found
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
Enlarging instruction streams
The stream fetch engine is a high-performance fetch architecture based on the concept of an instruction stream. We call a sequence of instructions from the target of a taken branch to the next taken branch, potentially containing multiple basic blocks, a stream. The long length of instruction streams makes it possible for the stream fetch engine to provide a high fetch bandwidth and to hide the branch predictor access latency, leading to performance results close to a trace cache at a lower implementation cost and complexity. Therefore, enlarging instruction streams is an excellent way to improve the stream fetch engine. In this paper, we present several hardware and software mechanisms focused on enlarging those streams that finalize at particular branch types. However, our results point out that focusing on particular branch types is not a good strategy due to Amdahl's law. Consequently, we propose the multiple-stream predictor, a novel mechanism that deals with all branch types by combining single streams into long virtual streams. This proposal tolerates the prediction table access latency without requiring the complexity caused by additional hardware mechanisms like prediction overriding. Moreover, it provides high-performance results which are comparable to state-of-the-art fetch architectures but with a simpler design that consumes less energy.Peer ReviewedPostprint (published version
SurveyMan: Programming and Automatically Debugging Surveys
Surveys can be viewed as programs, complete with logic, control flow, and
bugs. Word choice or the order in which questions are asked can unintentionally
bias responses. Vague, confusing, or intrusive questions can cause respondents
to abandon a survey. Surveys can also have runtime errors: inattentive
respondents can taint results. This effect is especially problematic when
deploying surveys in uncontrolled settings, such as on the web or via
crowdsourcing platforms. Because the results of surveys drive business
decisions and inform scientific conclusions, it is crucial to make sure they
are correct.
We present SurveyMan, a system for designing, deploying, and automatically
debugging surveys. Survey authors write their surveys in a lightweight
domain-specific language aimed at end users. SurveyMan statically analyzes the
survey to provide feedback to survey authors before deployment. It then
compiles the survey into JavaScript and deploys it either to the web or a
crowdsourcing platform. SurveyMan's dynamic analyses automatically find survey
bugs, and control for the quality of responses. We evaluate SurveyMan's
algorithms analytically and empirically, demonstrating its effectiveness with
case studies of social science surveys conducted via Amazon's Mechanical Turk.Comment: Submitted version; accepted to OOPSLA 201
DIA: A complexity-effective decoding architecture
Fast instruction decoding is a true challenge for the design of CISC microprocessors implementing variable-length instructions. A well-known solution to overcome this problem is caching decoded instructions in a hardware buffer. Fetching already decoded instructions avoids the need for decoding them again, improving processor performance. However, introducing such special--purpose storage in the processor design involves an important increase in the fetch architecture complexity. In this paper, we propose a novel decoding architecture that reduces the fetch engine implementation cost. Instead of using a special-purpose hardware buffer, our proposal stores frequently decoded instructions in the memory hierarchy. The address where the decoded instructions are stored is kept in the branch prediction mechanism, enabling it to guide our decoding architecture. This makes it possible for the processor front end to fetch already decoded instructions from the memory instead of the original nondecoded instructions. Our results show that using our decoding architecture, a state-of-the-art superscalar processor achieves competitive performance improvements, while requiring less chip area and energy consumption in the fetch architecture than a hardware code caching mechanism.Peer ReviewedPostprint (published version
Network hierarchy evolution and system vulnerability in power grids
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The seldom addressed network hierarchy property and its relationship with vulnerability analysis for power transmission grids from a complex-systems point of view are given in this paper. We analyze and compare the evolution of network hierarchy for the dynamic vulnerability evaluation of four different power transmission grids of real cases. Several meaningful results suggest that the vulnerability of power grids can be assessed by means of a network hierarchy evolution analysis. First, the network hierarchy evolution may be used as a novel measurement to quantify the robustness of power grids. Second, an antipyramidal structure appears in the most robust network when quantifying cascading failures by the proposed hierarchy metric. Furthermore, the analysis results are also validated and proved by empirical reliability data. We show that our proposed hierarchy evolution analysis methodology could be used to assess the vulnerability of power grids or even other networks from a complex-systems point of view.Peer ReviewedPostprint (author's final draft
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