Publications by Irem Boybat Kara

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

The computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for High Resolution (HR) images. Thi ...

Irem Boybat Kara

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

A phase-change memory model for neuromorphic computing

Mixed-Precision Deep Learning Based on Computational Memory

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

Multi-memristive synaptic architectures for training neural networks

Accurate deep neural network inference using computational phase-change memory

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

Phase-Change Memory Models for Deep Learning Training and Inference

Computational memory-based inference and training of deep neural networks

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

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Multi-ReRAM synapses for artificial neural network training

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

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

A Hardware-Oriented Dynamically Adaptive Disparity Estimation Algorithm and its Real-Time Hardware