Publications by Irem Boybat Kara

Related publications (22)

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

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

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

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Abu Sebastian

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

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

Irem Boybat Kara, Abu Sebastian

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

Multi-memristive synaptic architectures for training neural networks

Irem Boybat Kara

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Abu Sebastian

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

Neuromorphic Computing Using Non-Volatile Memory

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

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

Which Coupled is Best Coupled? An Exploration of AIMC Tile Interfaces and Load Balancing for CNNs

David Atienza Alonso, Giovanni Ansaloni, Irem Boybat Kara, Joshua Alexander Harrison Klein

Due to stringent energy and performance constraints, edge AI computing often employs heterogeneous systems that utilize both general-purpose CPUs and accelerators. Analog in-memory computing (AIMC) is a well-known AI inference solution that overcomes compu ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Phase-Change Memory Models for Deep Learning Training and Inference

Irem Boybat Kara, Abu Sebastian

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

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

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

Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues

Irem Boybat Kara

A large-scale artificial neural network, a three-layer perceptron, is implemented using two phase-change memory (PCM) devices to encode the weight of each of 164,885 synapses. The PCM conductances are programmed using a crossbar-compatible pulse scheme, an ...

IEEE2015

Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Abu Sebastian

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

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

Irem Boybat Kara, Abu Sebastian

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

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Irem Boybat Kara

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

Large-scale neural networks implemented with nonvolatile memory as the synaptic weight element: comparative performance analysis (accuracy, speed, and power)

Yusuf Leblebici, Irem Boybat Kara, Severin Sidler

We review our work towards achieving competitive performance (classification accuracies) for on chip machine learning (ML) of large scale artificial neural networks (ANN) using Non-Volatile Memory (NVM) based synapses, despite the inherent random and deter ...

2015

Multi-ReRAM synapses for artificial neural network training

Yusuf Leblebici, Irem Boybat Kara, Abu Sebastian, Elmira Shahrabi, Igor Krawczuk, Cecilia Giovinazzo

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

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Irem Boybat Kara, Flavio Ponzina, Joshua Alexander Harrison Klein, Yuxuan Wang, Abu Sebastian

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Institute of Electrical and Electronics Engineers (IEEE)2025

PCM for Neuromorphic Applications: Impact of Device Characteristics on Neural Network Performance

Yusuf Leblebici, Irem Boybat Kara, Severin Sidler

The impact of Phase Change Memory (PCM) as well as other Non-Volatile Memory (NVM) device characteristics on the quantitative classification performance of artificial neural networks is studied. Our results show that any NVM-based neural network — not just ...

2015

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

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

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

Impact of conductance drift on multi-PCM synaptic architectures

Yusuf Leblebici, Irem Boybat Kara, 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