28,478 research outputs found
CERN openlab Whitepaper on Future IT Challenges in Scientific Research
This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates
NiftyNet: a deep-learning platform for medical imaging
Medical image analysis and computer-assisted intervention problems are
increasingly being addressed with deep-learning-based solutions. Established
deep-learning platforms are flexible but do not provide specific functionality
for medical image analysis and adapting them for this application requires
substantial implementation effort. Thus, there has been substantial duplication
of effort and incompatible infrastructure developed across many research
groups. This work presents the open-source NiftyNet platform for deep learning
in medical imaging. The ambition of NiftyNet is to accelerate and simplify the
development of these solutions, and to provide a common mechanism for
disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical
imaging applications including segmentation, regression, image generation and
representation learning applications. Components of the NiftyNet pipeline
including data loading, data augmentation, network architectures, loss
functions and evaluation metrics are tailored to, and take advantage of, the
idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D
and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using
NiftyNet: (1) segmentation of multiple abdominal organs from computed
tomography; (2) image regression to predict computed tomography attenuation
maps from brain magnetic resonance images; and (3) generation of simulated
ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning
solutions for segmentation, regression, image generation and representation
learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge
Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6
figures; Update includes additional applications, updated author list and
formatting for journal submissio
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrated
state-of-the-art performance in various Artificial Intelligence tasks. To
accelerate the experimentation and development of CNNs, several software
frameworks have been released, primarily targeting power-hungry CPUs and GPUs.
In this context, reconfigurable hardware in the form of FPGAs constitutes a
potential alternative platform that can be integrated in the existing deep
learning ecosystem to provide a tunable balance between performance, power
consumption and programmability. In this paper, a survey of the existing
CNN-to-FPGA toolflows is presented, comprising a comparative study of their key
characteristics which include the supported applications, architectural
choices, design space exploration methods and achieved performance. Moreover,
major challenges and objectives introduced by the latest trends in CNN
algorithmic research are identified and presented. Finally, a uniform
evaluation methodology is proposed, aiming at the comprehensive, complete and
in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal,
201
Approaching delivery as a service
This paper explores the new logistics business model of Delivery as a Service, a concept aiming at a more efficient, fast and customer-oriented practice, linking IT solution development, urban logistics operations, supply chain efficiency and new business models. Delivery as a Service (DaaS) is defined as a service-oriented delivery and business processes in line with customer expectations and needs in the on-demand economy. The approach of this paper is an industry report based on evidence collected in multiple exploratory European projects integrating ambitious and strategic findings on Internet of Things, urban planning, consolidation centres, transport optimisation, and clean vehicle use. It contributes to a future scenario of urban logistics business models
VIoLET: A Large-scale Virtual Environment for Internet of Things
IoT deployments have been growing manifold, encompassing sensors, networks,
edge, fog and cloud resources. Despite the intense interest from researchers
and practitioners, most do not have access to large-scale IoT testbeds for
validation. Simulation environments that allow analytical modeling are a poor
substitute for evaluating software platforms or application workloads in
realistic computing environments. Here, we propose VIoLET, a virtual
environment for defining and launching large-scale IoT deployments within cloud
VMs. It offers a declarative model to specify container-based compute resources
that match the performance of the native edge, fog and cloud devices using
Docker. These can be inter-connected by complex topologies on which
private/public networks, and bandwidth and latency rules are enforced. Users
can configure synthetic sensors for data generation on these devices as well.
We validate VIoLET for deployments with > 400 devices and > 1500 device-cores,
and show that the virtual IoT environment closely matches the expected compute
and network performance at modest costs. This fills an important gap between
IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European
Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31,
2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for
presentation at the Plenary Session of the conferenc
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