Resistive Programmable Through Silicon Vias for Reconfigurable 3D Fabrics

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Multi-memristive synaptic architectures for training neural networks

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

Distributed Computing with Modern Shared Memory

Mihail Igor Zablotchi

In this thesis, we revisit classic problems in shared-memory distributed computing through the lenses of (1) emerging hardware technologies and (2) changing requirements. Our contributions consist, on the one hand, in providing a better understanding of th ...

EPFL2020

X-ray absorption linear dichroism at the Ti K-edge of rutile (001) TiO2 single crystal

Majed Chergui, Giulia Fulvia Mancini, Thomas Charles Henry Rossi, Dominik Kinschel, Oliviero Cannelli, Camila Bacellar Cases Da Silveira, Jérémy Raymond Jean Maurice Rouxel

X-ray absorption linear dichroism of rutile TiO2 at the Ti K-edge provides information about the electronic states involved in the pre-edge transitions. Here, linear dichroism with high energy resolution is analyzed in combination with ab initio finite dif ...

INT UNION CRYSTALLOGRAPHY2020

Analog Control of Retainable Resistance Multistates in HfO2 Resistive-Switching Random Access Memories (ReRAMs)

Yusuf Leblebici, Jury Sandrini, Elmira Shahrabi, Carlo Ricciardi, Cecilia Giovinazzo, Oguz Tolga Celik

Resistive-switching random access memory (ReRAM) technologies are nowadays a good candidate to overcome the bottleneck of Von Neumann architectures, taking advantage of their logic-in-memory capability and the ability to mimic biological synapse behavior. ...

AMER CHEMICAL SOC2019

Material and Electrical study of HfO2-Based Resistive Random Access Memories (ReRAMs)

Behnoush Attarimashalkoubeh

Resistive Random Access Memories (ReRAMs) have been researched intensively in the last past decades as a promising alternatives technology for the next-generation non-volatile memory (NVM) devices. ReRAMâ s excellent performance properties such as high sw ...

EPFL2019

ReRAM From Material Study to CMOS Co-integration

Elmira Shahrabi

The revolution of information-technology owes to silicon-based complementary-metal-oxide (CMOS) technology. However, CMOS technology approaches its physical limitation hardening the further progress of memory devices as well as computing paradigm requiring ...

EPFL2019

Performance improvement of chip-level CMOS-integrated ReRAM cells through material optimization

Yusuf Leblebici, Thomas La Grange, Elmira Shahrabi, Tugba Demirci

The integration of the resistive random access memory (ReRAM) with CMOS logic circuitry provides a solution to scaling limitations, and offers promising candidates for use in next generation computing applications. It is challenging to realize a reliable, ...

ELSEVIER SCIENCE BV2019

Switching event detection and self-termination programming circuit for energy efficient ReRAM memory arrays

Etienne Nowak, Alexandre Sébastien Julien Levisse

Energy efficiency remains a challenge for the design of non-volatile resistive memories (ReRAMs) arrays. This memory technology suffers from intrinsic variability in switching speed, programming voltages and resistance levels. The programming conditions of ...

2019

Training fully connected networks with resistive memories: impact of device failures

Massimo Giordano, Martina Bodini, Giorgio Cristiano

Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We rece ...

2019

Exploration of Negative Capacitance Devices and Technologies

Ali Saeidi

Conventional device scaling has been the main guiding principle of the MOS device engineering over these past years. However, this aggressive scaling would be eventually limited due to the inability to remove the heat generated by MOSFET devices. The power ...

EPFL2019

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

David Atienza Alonso, Xifan Tang, Pierre-Emmanuel Julien Marc Gaillardon, Edouard Giacomin, Marina Zapater Sancho, Yasir Mahmood Qureshi, João Miguel Morgado Pereira Vieira

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

2019

Radiation enhanced oxidation of proton-irradiated copper thin-films: Towards a new concept of ultra-high radiation dosimetry

Didier Bouvet, Jean-Michel Sallese, Georgi Gorine, Federico Ravotti

The effects of extreme radiation levels on the electrical resistivity of metal thin films made of copper were studied by means of electrical measurements and post irradiation imaging. Different 3x3 mm(2) chips were produced by depositing 500 nm of meander ...

2019

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