178,812 research outputs found
On Offline Evaluation of Vision-based Driving Models
Autonomous driving models should ideally be evaluated by deploying them on a
fleet of physical vehicles in the real world. Unfortunately, this approach is
not practical for the vast majority of researchers. An attractive alternative
is to evaluate models offline, on a pre-collected validation dataset with
ground truth annotation. In this paper, we investigate the relation between
various online and offline metrics for evaluation of autonomous driving models.
We find that offline prediction error is not necessarily correlated with
driving quality, and two models with identical prediction error can differ
dramatically in their driving performance. We show that the correlation of
offline evaluation with driving quality can be significantly improved by
selecting an appropriate validation dataset and suitable offline metrics. The
supplementary video can be viewed at
https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc
REPP-H: runtime estimation of power and performance on heterogeneous data centers
Modern data centers increasingly demand improved performance with minimal power consumption. Managing the power and performance requirements of the applications is challenging because these data centers, incidentally or intentionally, have to deal with server architecture heterogeneity [19], [22]. One critical challenge that data centers have to face is how to manage system power and performance given the different application behavior across multiple different architectures.This work has been supported by the EU FP7 program (Mont-Blanc 2, ICT-610402), by the
Ministerio de Economia (CAP-VII, TIN2015-65316-P), and the Generalitat de Catalunya (MPEXPAR, 2014-SGR-1051).
The material herein is based in part upon work supported by the US NSF, grant numbers ACI-1535232 and CNS-1305220.Peer ReviewedPostprint (author's final draft
Automated Instruction Stream Throughput Prediction for Intel and AMD Microarchitectures
An accurate prediction of scheduling and execution of instruction streams is
a necessary prerequisite for predicting the in-core performance behavior of
throughput-bound loop kernels on out-of-order processor architectures. Such
predictions are an indispensable component of analytical performance models,
such as the Roofline and the Execution-Cache-Memory (ECM) model, and allow a
deep understanding of the performance-relevant interactions between hardware
architecture and loop code. We present the Open Source Architecture Code
Analyzer (OSACA), a static analysis tool for predicting the execution time of
sequential loops comprising x86 instructions under the assumption of an
infinite first-level cache and perfect out-of-order scheduling. We show the
process of building a machine model from available documentation and
semi-automatic benchmarking, and carry it out for the latest Intel Skylake and
AMD Zen micro-architectures. To validate the constructed models, we apply them
to several assembly kernels and compare runtime predictions with actual
measurements. Finally we give an outlook on how the method may be generalized
to new architectures.Comment: 11 pages, 4 figures, 7 table
Automatic Throughput and Critical Path Analysis of x86 and ARM Assembly Kernels
Useful models of loop kernel runtimes on out-of-order architectures require
an analysis of the in-core performance behavior of instructions and their
dependencies. While an instruction throughput prediction sets a lower bound to
the kernel runtime, the critical path defines an upper bound. Such predictions
are an essential part of analytic (i.e., white-box) performance models like the
Roofline and Execution-Cache-Memory (ECM) models. They enable a better
understanding of the performance-relevant interactions between hardware
architecture and loop code. The Open Source Architecture Code Analyzer (OSACA)
is a static analysis tool for predicting the execution time of sequential
loops. It previously supported only x86 (Intel and AMD) architectures and
simple, optimistic full-throughput execution. We have heavily extended OSACA to
support ARM instructions and critical path prediction including the detection
of loop-carried dependencies, which turns it into a versatile
cross-architecture modeling tool. We show runtime predictions for code on Intel
Cascade Lake, AMD Zen, and Marvell ThunderX2 micro-architectures based on
machine models from available documentation and semi-automatic benchmarking.
The predictions are compared with actual measurements.Comment: 6 pages, 3 figure
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
Sentiment analysis of online user generated content is important for many
social media analytics tasks. Researchers have largely relied on textual
sentiment analysis to develop systems to predict political elections, measure
economic indicators, and so on. Recently, social media users are increasingly
using images and videos to express their opinions and share their experiences.
Sentiment analysis of such large scale visual content can help better extract
user sentiments toward events or topics, such as those in image tweets, so that
prediction of sentiment from visual content is complementary to textual
sentiment analysis. Motivated by the needs in leveraging large scale yet noisy
training data to solve the extremely challenging problem of image sentiment
analysis, we employ Convolutional Neural Networks (CNN). We first design a
suitable CNN architecture for image sentiment analysis. We obtain half a
million training samples by using a baseline sentiment algorithm to label
Flickr images. To make use of such noisy machine labeled data, we employ a
progressive strategy to fine-tune the deep network. Furthermore, we improve the
performance on Twitter images by inducing domain transfer with a small number
of manually labeled Twitter images. We have conducted extensive experiments on
manually labeled Twitter images. The results show that the proposed CNN can
achieve better performance in image sentiment analysis than competing
algorithms.Comment: 9 pages, 5 figures, AAAI 201
- …