Irem Boybat Kara

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Related publications (25)

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

Impact of conductance drift on multi-PCM synaptic architectures

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

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

Equivalent-accuracy accelerated neural-network training using analogue memory

Irem Boybat Kara, Severin Sidler, Carmelo Di Nolfo

2018

Neuromorphic computing with multi-memristive synapses

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

Nature Publishing Group2018

Signal and noise extraction from analog memory elements for neuromorphic computing

Irem Boybat Kara, Abu Sebastian, Sunghwan Kim

Nature Research2018

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

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

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, Evangelos Eleftheriou, Abu Sebastian

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

Neuromorphic Computing Using Non-Volatile Memory

Yusuf Leblebici, Irem Boybat Kara, Alessandro Fumarola, Seyoung Kim, Abu Sebastian, Severin Sidler

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to thre ...

2016

Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: impact of conductance response

Yusuf Leblebici, Irem Boybat Kara, Alessandro Fumarola, Severin Sidler

We assess the impact of the conductance response of Non-Volatile Memory (NVM) devices employed as the synaptic weight element for on-chip acceleration of the training of large-scale artificial neural networks (ANN). We briefly review our previous work towa ...

Ieee2016

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Irem Boybat Kara, Severin Sidler, Carmelo Di Nolfo

Using two phase-change memory devices per synapse, a three-layer perceptron network with 164 885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for nonvolatile memory (NV ...

Ieee-Inst Electrical Electronics Engineers Inc2015

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Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: impact of conductance response

Yusuf Leblebici, Irem Boybat Kara, Alessandro Fumarola, Severin Sidler

Ieee2016

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Irem Boybat Kara, Severin Sidler, Carmelo Di Nolfo

Ieee-Inst Electrical Electronics Engineers Inc2015