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Author Azghadi, Mostafa Rahimi ♦ Moradi, Saber ♦ Fasnacht, Daniel B ♦ Ozdas, Mehmet Sirin ♦ Indiveri, Giacomo
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword AER ♦ STDP ♦ VLSI ♦ Asynchronous ♦ Emerging technologies ♦ Learning ♦ Neuromorphic ♦ Plasticity ♦ Realtime ♦ Subthreshold
Abstract Hardware implementations of spiking neural networks offer promising solutions for computational tasks that require compact and low-power computing technologies. As these solutions depend on both the specific network architecture and the type of learning algorithm used, it is important to develop spiking neural network devices that offer the possibility to reconfigure their network topology and to implement different types of learning mechanisms. Here we present a neuromorphic multi-neuron VLSI device with on-chip programmable event-based hybrid analog/digital circuits; the event-based nature of the input/output signals allows the use of address-event representation infrastructures for configuring arbitrary network architectures, while the programmable synaptic efficacy circuits allow the implementation of different types of spike-based learning mechanisms. The main contributions of this article are to demonstrate how the programmable neuromorphic system proposed can be configured to implement specific spike-based synaptic plasticity rules and to depict how it can be utilised in a cognitive task. Specifically, we explore the implementation of different spike-timing plasticity learning rules online in a hybrid system comprising a workstation and when the neuromorphic VLSI device is interfaced to it, and we demonstrate how, after training, the VLSI device can perform as a standalone component (i.e., without requiring a computer), binary classification of correlated patterns.
Description Author Affiliation: University of Zurich, ETH Zurich, and University of Adelaide, Australia (Azghadi, Mostafa Rahimi); University of Zurich, ETH Zurich, Zurich, Switzerland (Moradi, Saber; Fasnacht, Daniel B; Ozdas, Mehmet Sirin; Indiveri, Giacomo)
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-09-02
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 12
Issue Number 2
Page Count 18
Starting Page 1
Ending Page 18


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Source: ACM Digital Library