8,396 research outputs found
Implementation and Deployment of a Library of the High-level Application Programming Interfaces (SemSorGrid4Env)
The high-level API service is designed to support rapid development of thin web applications and mashups beyond the state of the art in GIS, while maintaining compatibility with existing tools and expectations. It provides a fully configurable API, while maintaining a separation of concerns between domain experts, service administrators and mashup developers. It adheres to REST and Linked Data principles, and provides a novel bridge between standards-based (OGC O&M) and Semantic Web approaches. This document discusses the background motivations for the HLAPI (including experiences gained from any previously implemented versions), before moving onto specific details of the final implementation, including configuration and deployment instructions, as well as a full tutorial to assist mashup developers with using the exposed observation data
Active networks: an evolution of the internet
Active Networks can be seen as an evolution of the classical model of packet-switched networks. The traditional and ”passive” network model is based on a static definition of the network node behaviour. Active Networks propose an “active” model where the intermediate nodes (switches and routers) can load and execute user code contained in the data units (packets). Active Networks are a programmable network model, where bandwidth and computation are both considered shared network resources. This approach opens up new interesting research fields. This paper gives a short introduction of Active
Networks, discusses the advantages they introduce and presents the research advances in this field
Dynamic Power Management for Neuromorphic Many-Core Systems
This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
A model-driven approach to broaden the detection of software performance antipatterns at runtime
Performance antipatterns document bad design patterns that have negative
influence on system performance. In our previous work we formalized such
antipatterns as logical predicates that predicate on four views: (i) the static
view that captures the software elements (e.g. classes, components) and the
static relationships among them; (ii) the dynamic view that represents the
interaction (e.g. messages) that occurs between the software entities elements
to provide the system functionalities; (iii) the deployment view that describes
the hardware elements (e.g. processing nodes) and the mapping of the software
entities onto the hardware platform; (iv) the performance view that collects
specific performance indices. In this paper we present a lightweight
infrastructure that is able to detect performance antipatterns at runtime
through monitoring. The proposed approach precalculates such predicates and
identifies antipatterns whose static, dynamic and deployment sub-predicates are
validated by the current system configuration and brings at runtime the
verification of performance sub-predicates. The proposed infrastructure
leverages model-driven techniques to generate probes for monitoring the
performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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