The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Unlike general purpose computer architectures that are comprised of complex processor cores and sequential computation, the brain is innately parallel and contains highly complex connections between computational units (neurons). Key to the architecture of the brain is a functionality enabled by the combined effect of spiking communication and sparse connectivity with unique variable efficacies and...
Today's data centers, their handheld computers and network sensors, are discussed in terms of how they are penetrated by viruses and rootkits. This paper then presents a new computer architecture, implemented to be semantically compatible with an existing microprocessor, along with modification of several system components commonly found in data centers. The new computer architecture physically separates...
Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively. Convolutional Neural Networks have shown promise as baseline fixed feature extractors through transfer learning, a technique that helps minimize the training cost on visual information. Temporal information is often handled using hand-crafted features or Recurrent Neural...
Compact online learning architectures could be used to enhance internet of things devices to allow them to learn directly based on data being received instead of having to ship data to a remote server for learning. This saves communications energy and enhances privacy and security as the data is not shared. The learning architectures can also be used in high performance computing and in traditional...
A variety of architectures have been proposed for neuromorphic computing chips, including digital, analog, and memristor based approaches. The application space used to analyze these designs is typically narrow, focused primarily on natural signal processing tasks such as image or audio classification. In this work, we analyze the ability of a memristor-based neuromorphic architecture to perform tasks...
Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.