Analog VLSI on-chip learning Neural Networks represent a mature technology for a large
number of applications involving industrial as well as consumer appliances. This is …

Analog computing is attractive compared to digital computing due to its potential for
achieving higher computational density and higher energy efficiency. However, unlike digital …

Support vector machines are emerging as a powerful machine-learning tool. Logarithmic
number systems (LNS) utilize the property of logarithmic compression for numerical …

Modeling networks of spiking neurons is a common scientific method that helps to
understand how biological neural systems represent, process and store information. But the …

In this paper we present an analysis of the minimal hardware precision required to
implement support vector machine (SVM) classification within a logarithmic number system …

Future increases in computing power strongly rely on miniaturization, large scale integration,
and parallelization. Yet, approaching the nanometer realm poses new challenges in terms of …

Rein analoge und gemischt analog-digitale Realisierungen künstlicher neuronaler
Netzwerke in Hardware entziehen sich für gewöhnlich einer exakten quantitativen …

Neuroinspired computing and in particular the implementation of large scale spiking neural
networks (SNNs) on reconfigurable platforms such as Field Programmable Gate Arrays …

Low Power Techniques and Neural Applications in Microelectronics — Universitat Autònoma
de Barcelona Research Portal Skip to main navigation Skip to search Skip to main content …