Concept

Neuromorphic engineering

Publications associées (98)

Slasher: Stadium racer car for event camera end-to-end learning autonomous driving experiments

Slasher is the first open 1/10 scale autonomous driving platform for exploring the use of neuromorphic event cameras for fast driving in unstructured indoor and outdoor environments. Slasher features a DAVIS event-based camera and ROS computer for percepti ...

2019

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

Olivier Richard Hagens

The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic m ...

2019

VO2 oscillators coupling for Neuromorphic Computation

Mihai Adrian Ionescu, Igor Stolichnov, Kirsten Emilie Moselund, Siegfried Karg, Elisabetta Corti

New computation schemes inspired by biological processes can outperform standard von-Neumann architectures in dealing with complex and unstructured tasks. As a new approach, systems of frequency-locked, coupled oscillators are investigated using the phase ...

IEEE2019

Multi-ReRAM synapses for artificial neural network training

Elmira Shahrabi, Igor Krawczuk, Yusuf Leblebici, Irem Boybat Kara, Carlo Ricciardi, Cecilia Giovinazzo, Evangelos Eleftheriou, Abu Sebastian

Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elements of neuromorphic co-processors for training deep neural networks (DNNs). However, device-level non-idealities are posing significant challenges. In this wo ...

IEEE2019

Multi-panel, on-single-chip Memristive Biosensing

Giovanni De Micheli, Sandro Carrara, Danilo Demarchi, Ioulia Tzouvadaki, Abuduwaili Tuoheti

Memristive biosensors have demonstrated excellent capabilities for ultrasensitive bio-detection. In the present work, memristive biosensing chips are designed, fabricated and implemented in a for the first time presented multi-panel on-chip detection for d ...

2019

Portable Memristive Biosensing System as Effective Point-of-Care Device for Cancer Diagnostics

Giovanni De Micheli, Sandro Carrara, Danilo Demarchi, Ioulia Tzouvadaki, Abuduwaili Tuoheti

Memristive biosensors have been proved as excellent candidates for ultrasensitive biosensing. In this work, a novel portable bio-detection system based on memristive biosensors is designed, developed and tested for providing a significantly fast, automatic ...

IEEE2018

Unsupervised Learning of Phase-Change-Based Neuromorphic Systems

Stanislaw Andrzej Wozniak

Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

EPFL2017

Neuromorphic Architecture with 1M Memristive Synapses for Detection of Weakly Correlated Inputs

Yusuf Leblebici, Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Severin Sidler, Angeliki Pantazi

Neuromorphic computing takes inspiration from the brain to build highly parallel, energy- and area-efficient architectures. Recently, hardware realizations of neurons and synapses using memristive devices were proposed and applied for the task of correlati ...

Ieee-Inst Electrical Electronics Engineers Inc2017

Neuromorphic System with Phase-Change Synapses for Pattern Learning and Feature Extraction

Yusuf Leblebici, Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Angeliki Pantazi

Neuromorphic systems provide biologically inspired methods of computing, alternative to the classical von Neumann approach. In these systems, computation is performed by a network of spiking neurons controlled by the values of their synaptic weights, which ...

2017

Unsupervised Learning Using Phase-Change Synapses and Complementary Patterns

Yusuf Leblebici, Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Severin Sidler, Angeliki Pantazi

Neuromorphic systems using memristive devices provide a brain-inspired alternative to the classical von Neumann processor architecture. In this work, a spiking neural network (SNN) implemented using phase-change synapses is studied. The network is equipped ...

2017

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