Publication

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Publications associées (89)

Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Evangelos Eleftheriou, 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

An improved objective metric to predict image quality using deep neural networks

Touradj Ebrahimi, Pinar Akyazi

Objective quality assessment of compressed images is very useful in many applications. In this paper we present an objective quality metric that is better tuned to evaluate the quality of images distorted by compression artifacts. A deep convolutional neur ...

2019

Estimation of groundwater storage from seismic data using deep learning

Jan Sickmann Hesthaven

We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks a ...

Wiley2019

Analog Neural Networks with Deep-submicron Nonlinear Synapses

Pascal Frossard, Babak Falsafi, Ahmet Caner Yüzügüler, Mario Paulo Drumond Lages De Oliveira, Firat Çelik

Deep neural network (DNN) inference tasks are computationally expensive. Digital DNN accelerators offer better density and energy efficiency than general-purpose processors but still not sufficient to be deployable on resource-constrained settings. Analog ...

2019

Analog Neural Networks with Deep-submicron Nonlinear Synapses

Pascal Frossard, Babak Falsafi, Ahmet Caner Yüzügüler, Mario Paulo Drumond Lages De Oliveira, Firat Çelik

2019

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

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband

Alexios Konstantinos Balatsoukas Stimming

Digital predistortion is the process of using digital signal processing to correct nonlinearities caused by the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude o ...

IEEE2019

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

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Giorgio Cristiano, Massimo Giordano

Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)-such as Phase-Change Memory or Resistive Memory-can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computatio ...

2018

CNN based Query by Example Spoken Term Detection

Hervé Bourlard, Dhananjay Ram

In this work, we address the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State of the art solutions usually rely on dynamic time warping (DTW) based template matching. In contrast, we propose here to tackle the pr ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

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