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.
Scalability is a key challenge for digital spiking neural networks (SNN) in hardware. This paper proposes an efficient neuron architecture (ENA) to reduce the silicon area occupied by neurons. As the computation resource (e.g. DSP in FPGAs) is limited for hardware SNNs, the proposed ENA employs a sharing mechanism of computing component at two levels (synapse and neuron) to reduce the occupied resources...
A Self-rePAiring spiking Neural NEtwoRk (SPANNER) hardware architecture is presented in this paper. It is based on a software model of an astrocyte-neuron network which previously demonstrated the ability to self-detect faults and self-repair autonomously. Experimental results in this paper show that when faults occur at the synapse, remaining healthy synapses of the same neuron are enhanced by the...
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.