Publications by Severin Sidler

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

Yusuf Leblebici, Severin Sidler, Alessandro Fumarola

Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, ...

IEEE2019

Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part II: Impact of Al/Mo/Pr0.7Ca0.3MnO3 Device Characteristics on Neural Network Training Accuracy

Yusuf Leblebici, Severin Sidler, Alessandro Fumarola

2018

Equivalent-accuracy accelerated neural-network training using analogue memory

Severin Sidler, Irem Boybat Kara, Carmelo Di Nolfo

2018

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

Accelerating Machine Learning with Non-Volatile Memory: exploring device and circuit tradeoffs

Severin Sidler, Alessandro Fumarola

Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductanc ...

Ieee2016

Neuromorphic Computing Using Non-Volatile Memory

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

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, Severin Sidler, Irem Boybat Kara, 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

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

Severin Sidler, Irem Boybat Kara, 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

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

Yusuf Leblebici, Severin Sidler, Irem Boybat Kara, Carmelo Di Nolfo

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

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

Yusuf Leblebici, Severin Sidler, Irem Boybat Kara, Carmelo Di Nolfo

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