The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform

T. Herman - One of the best experts on this subject based on the ideXlab platform.

  • Phase Clocks for Transient Fault Repair
    arXiv: Distributed Parallel and Cluster Computing, 2000
    Co-Authors: T. Herman
    Abstract:

    Phase clocks are synchronization tools that implement a form of logical time in distributed systems. For systems tolerating Transient faults by self-repair of damaged data, phase clocks can enable reasoning about the progress of distributed repair procedures. This paper presents a phase clock algorithm suited to the model of Transient Memory faults in asynchronous systems with read/write registers. The algorithm is self-stabilizing and guarantees accuracy of phase clocks within O(k) time following an initial state that is k-faulty. Composition theorems show how the algorithm can be used for the timing of distributed procedures that repair system outputs.

  • Phase clocks for Transient fault repair
    IEEE Transactions on Parallel and Distributed Systems, 2000
    Co-Authors: T. Herman
    Abstract:

    Phase clocks are synchronization tools that implement a form of logical time in distributed systems. For systems tolerating Transient faults by self-repair of damaged data, phase clocks can enable reasoning about the progress of distributed repair procedures. This paper presents a phase clock algorithm suited to the model of Transient Memory faults in asynchronous systems with read/write registers. The algorithm is self-stabilizing and guarantees accuracy of phase clocks within O(k) time following an initial state that is.

Pablo Varona - One of the best experts on this subject based on the ideXlab platform.

  • IWINAC (2) - Local context discrimination in signature neural networks
    New Challenges on Bioinspired Applications, 2011
    Co-Authors: Roberto Latorre, Francisco B. Rodriguez, Pablo Varona
    Abstract:

    Bio-inspiration in traditional artificial neural networks (ANN) relies on knowledge about the nervous system that was available more than 60 years ago. Recent findings from neuroscience research provide novel elements of inspiration for ANN paradigms. We have recently proposed a Signature Neural Network that uses: (i) neural signatures to identify each unit in the network, (ii) local discrimination of input information during the processing, and (iii) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local Transient Memory. In this paper we further analyze the role of this local context Memory to efficiently solve jigsaw puzzles.

  • Signature Neural Networks: Definition and Application to Multidimensional Sorting Problems
    IEEE Transactions on Neural Networks, 2011
    Co-Authors: Roberto Latorre, Francisco De Borja Rodríguez, Pablo Varona
    Abstract:

    In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: (1) neural signatures to identify each unit in the network; (2) local discrimination of input information during the processing; and (3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local Transient Memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

  • history dependent excitability as a single cell substrate of Transient Memory for information discrimination
    PLOS ONE, 2010
    Co-Authors: Fabiano Baroni, Joaquin J Torres, Pablo Varona
    Abstract:

    Neurons react differently to incoming stimuli depending upon their previous history of stimulation. This property can be considered as a single-cell substrate for Transient Memory, or context-dependent information processing: depending upon the current context that the neuron “sees” through the subset of the network impinging on it in the immediate past, the same synaptic event can evoke a postsynaptic spike or just a subthreshold depolarization. We propose a formal definition of History-Dependent Excitability (HDE) as a measure of the propensity to firing in any moment in time, linking the subthreshold history-dependent dynamics with spike generation. This definition allows the quantitative assessment of the intrinsic Memory for different single-neuron dynamics and input statistics. We illustrate the concept of HDE by considering two general dynamical mechanisms: the passive behavior of an Integrate and Fire (IF) neuron, and the inductive behavior of a Generalized Integrate and Fire (GIF) neuron with subthreshold damped oscillations. This framework allows us to characterize the sensitivity of different model neurons to the detailed temporal structure of incoming stimuli. While a neuron with intrinsic oscillations discriminates equally well between input trains with the same or different frequency, a passive neuron discriminates better between inputs with different frequencies. This suggests that passive neurons are better suited to rate-based computation, while neurons with subthreshold oscillations are advantageous in a temporal coding scheme. We also address the influence of intrinsic properties in single-cell processing as a function of input statistics, and show that intrinsic oscillations enhance discrimination sensitivity at high input rates. Finally, we discuss how the recognition of these cell-specific discrimination properties might further our understanding of neuronal network computations and their relationships to the distribution and functional connectivity of different neuronal types.

Marco Caccamo - One of the best experts on this subject based on the ideXlab platform.

  • A Reliable and Predictable Scratchpad-centric OS for Multi-core Embedded Systems
    2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2017
    Co-Authors: Rohan Tabish, Renato Mancuso, Saud Wasly, Sujit S. Phatak, Rodolfo Pellizzoni, Marco Caccamo
    Abstract:

    The reliable use of multi-core platforms for designing safety-critical systems still represents an open challenge. Recently, the FAA [1] has formally expressed its concern towards the use of multi-core systems in avionics. The sharing of hardware resources introduces non-trivial timing dependencies between logically independent components (e.g. cores); additionally, the increase in size of circuitry, Memory resources, and transistor density makes these platforms more susceptible to Transient Memory (soft) errors. This work addresses the problem of Memory soft errors and their recovery at an OS/platform level on commercial multi-core systems. Proposed strategy considers the schedulability impact of recovery procedures on hard real-time workloads. Finally, the implementation of a SPM-centric OS with the proposed OS-level strategies was performed by using a commercially available multi-core platform. The design has been validated and evaluated using a combination of synthetic and realistic (EEMBC) benchmarks.

  • RTAS - A Reliable and Predictable Scratchpad-centric OS for Multi-core Embedded Systems
    2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2017
    Co-Authors: Rohan Tabish, Renato Mancuso, Saud Wasly, Sujit S. Phatak, Rodolfo Pellizzoni, Marco Caccamo
    Abstract:

    Abstract-The reliable use of multi-core platforms for designing safety-critical systems still represents an open challenge. Recently, the FAA [1] has formally expressed its concern towards the use of multi-core systems in avionics. The sharing of hardware resources introduces non-trivial timing dependencies between logically independent components (e.g. cores); additionally, the increase in size of circuitry, Memory resources, and transistor density makes these platforms more susceptible to Transient Memory (soft) errors. This work addresses the problem of Memory soft errors and their recovery at an OS/platform level on commercial multi-core systems. Proposed strategy considers the schedulability impact of recovery procedures on hard real-time workloads. Finally, the implementation of a SPM-centric OS with the proposed OS-level strategies was performed by using a commercially available multi-core platform. The design has been validated and evaluated using a combination of synthetic and realistic (EEMBC) benchmarks.

Carola Berking - One of the best experts on this subject based on the ideXlab platform.

  • Transient Memory impairment and Transient global amnesia induced by photodynamic therapy
    British Journal of Dermatology, 2015
    Co-Authors: Markus Reinholz, M Heppt, Franziska Hoffmann, N Lummel, T Ruzicka, P Lehmann, Carola Berking
    Abstract:

    Summary Photodynamic therapy (PDT) is a commonly used and effective treatment option for nonmelanoma skin cancer. Apart from local side-effects such as pain, oedema and erythema, no major adverse events occur in the majority of cases. Here we report on five patients who developed Memory deficits such as Transient global amnesia immediately after PDT for actinic keratosis. All PDT treatments were performed according to standard therapy protocols. The reported patients had a Memory gap for the entire procedure, as well as for the consecutive emergency medical care. Other common neurological causes such as stroke or epileptic seizures were excluded. No focal neurological deficits were detectable. The symptoms had a fairly rapid onset following red-light illumination and were reversible without sequelae within 1–24 h. No correlation of the condition and pain during the illumination could be revealed. Magnetic resonance imaging of the brain revealed punctuated lesions in the hippocampus as a potential morphological correlate in one patient. The association between amnestic syndromes and PDT is novel and has not previously been reported. Even though PDT is considered a safe treatment modality, the possibility of neurological adverse events, albeit rare, should be kept in mind.

Guru Venkataramani - One of the best experts on this subject based on the ideXlab platform.

  • Increasing Memory Utilization with Transient Memory Scheduling
    2012 IEEE 33rd Real-Time Systems Symposium, 2012
    Co-Authors: Qi Wang, Jiguo Song, Gabriel Parmer, Andrew Sweeney, Guru Venkataramani
    Abstract:

    In addition to predictability, both reliability and security are increasingly important for embedded systems. To limit the scope of errant behavior in open and mixed criticality systems, a common approach is to raise isolation barriers between software components. However, this decentralizes Memory management across all system components. Memory is often cached and quickly accessible in each application. This paper introduces the TMEM system for increasing Memory utilization while optimizing for application end-to-end constraints such as meeting deadlines. In addition to the traditional spatial multiplexing of Memory, TMEM introduces the predictable temporal multiplexing of Memory within caches in a system component, and Memory scheduling to continually reallocate Memory between components to best benefit the system. We find that TMEM is able to maintain the efficiency of caches, while also lowering both task tardiness and system Memory requirements.