Publications de Evangelos Eleftheriou

Approximating Relu Networks By Single-Spike Computation

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Angeliki Pantazi, Ana Stanojevic, Evangelos Eleftheriou, Giovanni Cherubini

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 ...

IEEE2022

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require rea ...

IEEE2020

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventio ...

Nature Research2020

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

Nature Research2020

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Irem Boybat Kara, Evangelos Eleftheriou

In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...

IEEE2020

Phase-Change Memory Models for Deep Learning Training and Inference

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Non-volatile analog memory devices such as phase-change memory (PCM) enable designing dedicated connectivity matrices for the hardware implementation of deep neural networks (DNN). In this in-memory computing approach, the analog conductance states of the ...

IEEE2019

Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

IEEE2019

Multi-ReRAM synapses for artificial neural network training

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

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

Mixed-precision architecture based on computational memory for training deep neural networks

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Deep neural networks (DNN) have revolutionized the field of machine learning by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. Training of large DNNs, however, is a computationally in ...

IEEE2018

A phase-change memory model for neuromorphic computing

Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels i ...

AMER INST PHYSICS2018

Impact of conductance drift on multi-PCM synaptic architectures

Yusuf Leblebici, Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

In-memory computing with nanoscale memristive devices such as phase-change memory (PCM) has emerged as an alternative to conventional von Neumann systems to train deep neural networks (DNN) where a synaptic weight is represented by the device conductance. ...

IEEE2018

Neuromorphic computing with multi-memristive synapses

Yusuf Leblebici, Irem Boybat Kara, Abu Sebastian, Evangelos Eleftheriou

Nature Publishing Group2018

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

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

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

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

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

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

Unsupervised Learning Using Phase-Change Synapses and Complementary Patterns

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

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

All-memristive neuromorphic computing with level-tuned neurons

Stanislaw Andrzej Wozniak, Angeliki Pantazi, Evangelos Eleftheriou

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 Ltd2016

Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Stanislaw Andrzej Wozniak, Angeliki Pantazi, Evangelos Eleftheriou

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

Multilevel-Cell Phase-Change Memory: A Viable Technology

Milos Stanisavljevic, Aravinthan Athmanathan, Evangelos Eleftheriou

In order for any non-volatile memory (NVM) to be considered a viable technology, its reliability should be verified at the array level. In particular, properties such as high endurance and at least moderate data retention are considered essential. Phase-ch ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Compact Electro-Thermal Model for Thermal Cross-Talk Analysis in PCM Arrays

Yusuf Leblebici, Milos Stanisavljevic, Aravinthan Athmanathan, Abu Sebastian, Evangelos Eleftheriou

2013