16,523 research outputs found
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
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
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
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
CEP-DTHP : A Complex Event Processing using the Dual-Tier Hybrid Paradigm Over the Stream Mining Process
CEP is a widely used technique for the reliability and recognition of arbitrarily complex patterns in enormous data streams with great performance in real time. Real-time detection of crucial events and rapid response to them are the key goals of sophisticated event processing. The performance of event processing systems can be improved by parallelizing CEP evaluation procedures. Utilizing CEP in parallel while deploying a multi-core or distributed environment is one of the most popular and widely recognized tackles to accomplish the goal. This paper demonstrates the ability to use an unusual parallelization strategy to effectively process complicated events over streams of data. This method depends on a dual-tier hybrid paradigm that combines several parallelism levels. Thread-level or task-level parallelism (TLP) and Data-level parallelism (DLP) were combined in this research. Many threads or instruction sequences from a comparable application can run concurrently under the TLP paradigm. In the DLP paradigm, instruc-tions from a single stream operate on several data streams at the same time. In our suggested model, there are four major stages: data mining, pre-processing, load shedding, and optimization. The first phase is online data mining, following which the data is materialized into a publicly available solution that combines a CEP engine with a library. Next, data pre-processing encompasses the efficient adaptation of the content or format of raw data from many, perhaps diverse sources. Finally, parallelization approaches have been created to reduce CEP processing time. By providing this two-type parallelism, our proposed solution combines the benefits of DLP and TLP while addressing their constraints. The JAVA tool will be used to assess the suggested technique. The performance of the suggested technique is compared to that of other current ways for determining the efficacy and efficiency of the proposed algorithm
Transparently Mixing Undo Logs and Software Reversibility for State Recovery in Optimistic PDES
The rollback operation is a fundamental building block to support the correct execution of a speculative Time Warp-based Parallel Discrete Event Simulation. In the literature, several solutions to reduce the execution cost of this operation have been proposed, either based on the creation of a checkpoint of previous simulation state images, or on the execution of negative copies of simulation events which are able to undo the updates on the state. In this paper, we explore the practical design and implementation of a state recoverability technique which allows to restore a previous simulation state either relying on checkpointing or on the reverse execution of the state updates occurred while processing events in forward mode. Differently from other proposals, we address the issue of executing backward updates in a fully-transparent and event granularity-independent way, by relying on static software instrumentation (targeting the x86 architecture and Linux systems) to generate at runtime reverse update code blocks (not to be confused with reverse events, proper of the reverse computing approach). These are able to undo the effects of a forward execution while minimizing the cost of the undo operation. We also present experimental results related to our implementation, which is released as free software and fully integrated into the open source ROOT-Sim (ROme OpTimistic Simulator) package. The experimental data support the viability and effectiveness of our proposal
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
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Towards an aspect weaving BPEL engine
This position paper proposes the use of dynamic aspects and
the visitor design pattern to obtain a highly configurable and
extensible BPEL engine. Using these two techniques, the
core of this infrastructural software can be customised to
meet new requirements and add features such as debugging,
execution monitoring, or changing to another Web Service
selection policy. Additionally, it can easily be extended to
cope with customer-specific BPEL extensions. We propose
the use of dynamic aspects not only on the engine itself
but also on the workflow in order to tackle the problems of
Web Service hot deployment and hot fixes to long running
processes. In this way, composing aWeb Service "on-the-fly"
means weaving its choreography interface into the workflow
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