Personne
Angeliki Pantazi
Publications associées (7)
An exact mapping from ReLU networks to spiking neural networks
Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Giovanni Cherubini, Angeliki Pantazi
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...
2023Approximating Relu Networks By Single-Spike Computation
Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Evangelos Eleftheriou, Giovanni Cherubini, Angeliki Pantazi
Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...
IEEE2022Neuromorphic 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 ...
2017Unsupervised 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 ...
2017Neuromorphic 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 Inc2017All-memristive neuromorphic computing with level-tuned neurons
Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Angeliki Pantazi
In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from m ...
Iop Publishing Ltd2016Learning spatio-temporal patterns in the presence of input noise using phase-change memristors
Stanislaw Andrzej Wozniak, Evangelos Eleftheriou, Angeliki Pantazi
Neuromorphic systems increasingly attract research interest owing to their ability to provide biologically inspired methods of computing, alternative to the classic von Neumann architecture. In these systems, computing relies on spike-based communication b ...
2016