88,792 research outputs found
Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images
Recovering the 3D representation of an object from single-view or multi-view
RGB images by deep neural networks has attracted increasing attention in the
past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural
networks (RNNs) to fuse multiple feature maps extracted from input images
sequentially. However, when given the same set of input images with different
orders, RNN-based approaches are unable to produce consistent reconstruction
results. Moreover, due to long-term memory loss, RNNs cannot fully exploit
input images to refine reconstruction results. To solve these problems, we
propose a novel framework for single-view and multi-view 3D reconstruction,
named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse
3D volume from each input image. Then, a context-aware fusion module is
introduced to adaptively select high-quality reconstructions for each part
(e.g., table legs) from different coarse 3D volumes to obtain a fused 3D
volume. Finally, a refiner further refines the fused 3D volume to generate the
final output. Experimental results on the ShapeNet and Pix3D benchmarks
indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large
margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in
terms of backward inference time. The experiments on ShapeNet unseen 3D
categories have shown the superior generalization abilities of our method.Comment: ICCV 201
Computing server power modeling in a data center: survey,taxonomy and performance evaluation
Data centers are large scale, energy-hungry infrastructure serving the
increasing computational demands as the world is becoming more connected in
smart cities. The emergence of advanced technologies such as cloud-based
services, internet of things (IoT) and big data analytics has augmented the
growth of global data centers, leading to high energy consumption. This upsurge
in energy consumption of the data centers not only incurs the issue of surging
high cost (operational and maintenance) but also has an adverse effect on the
environment. Dynamic power management in a data center environment requires the
cognizance of the correlation between the system and hardware level performance
counters and the power consumption. Power consumption modeling exhibits this
correlation and is crucial in designing energy-efficient optimization
strategies based on resource utilization. Several works in power modeling are
proposed and used in the literature. However, these power models have been
evaluated using different benchmarking applications, power measurement
techniques and error calculation formula on different machines. In this work,
we present a taxonomy and evaluation of 24 software-based power models using a
unified environment, benchmarking applications, power measurement technique and
error formula, with the aim of achieving an objective comparison. We use
different servers architectures to assess the impact of heterogeneity on the
models' comparison. The performance analysis of these models is elaborated in
the paper
A Graph-Partition-Based Scheduling Policy for Heterogeneous Architectures
In order to improve system performance efficiently, a number of systems
choose to equip multi-core and many-core processors (such as GPUs). Due to
their discrete memory these heterogeneous architectures comprise a distributed
system within a computer. A data-flow programming model is attractive in this
setting for its ease of expressing concurrency. Programmers only need to define
task dependencies without considering how to schedule them on the hardware.
However, mapping the resulting task graph onto hardware efficiently remains a
challenge. In this paper, we propose a graph-partition scheduling policy for
mapping data-flow workloads to heterogeneous hardware. According to our
experiments, our graph-partition-based scheduling achieves comparable
performance to conventional queue-base approaches.Comment: Presented at DATE Friday Workshop on Heterogeneous Architectures and
Design Methods for Embedded Image Systems (HIS 2015) (arXiv:1502.07241
Contention-aware performance monitoring counter support for real-time MPSoCs
Tasks running in MPSoCs experience contention delays when accessing MPSoC’s shared resources, complicating task timing analysis and deriving execution time bounds. Understanding the Actual Contention Delay (ACD) each task suffers due to other corunning tasks, and the particular hardware shared resources in which contention occurs, is of prominent importance to increase confidence on derived execution time bounds of tasks. And, whenever those bounds are violated, ACD provides information on the reasons for overruns. Unfortunately, existing MPSoC designs considered in real-time domains offer limited hardware support to measure tasks’ ACD losing all these potential benefits. In this paper we propose the Contention Cycle Stack (CCS), a mechanism that extends performance monitoring counters to track specific events that allow estimating the ACD that each task suffers from every contending task on every hardware shared resource. We build the CCS using a set of specialized low-overhead Performance Monitoring Counters for the Cobham Gaisler GR740 (NGMP) MPSoC – used in the space domain – for which we show CCS’s benefits.The research leading to these results has received funding from the European Space Agency under contracts 4000109680,
4000110157 and NPI 4000102880, and the Ministry of Science and Technology of Spain under contract TIN-2015-65316-P.
Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717.Peer ReviewedPostprint (author's final draft
- …