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Publications associées (31)

Page 2 sur 2

Write Termination circuits for RRAM : A Holistic Approach From Technology to Application Considerations

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

While Resistive Random Access Memories (RRAM) are perceived nowadays as a promising solution for the future of computing, these technologies suffer from intrinsic variability regarding programming voltage, switching speed and achieved resistance values. Wr ...

IEEE2020

mu RAI: Securing Embedded Systems with Return Address Integrity

Mathias Josef Payer

Embedded systems are deployed in security critical environments and have become a prominent target for remote attacks. Microcontroller-based systems (MCUS) are particularly vulnerable due to a combination of limited resources and low level programming whic ...

INTERNET SOC2020

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

Intrinsic or nucleation-driven switching: An insight from nanoscopic analysis of negative capacitance Hf1-xZrxO2-based structures

Mihai Adrian Ionescu, Igor Stolichnov, Matteo Cavalieri, Carlotta Gastaldi

HfO2-based ferroelectrics have dramatically changed the application perspectives of polarization-switching materials for information processing and storage. Their CMOS compatibility and preservation of high reversible polarization down to a few nanometer t ...

AMER INST PHYSICS2020

Gain-Cell Embedded DRAMs: Modeling and Design Space

Andreas Peter Burg, Robert Giterman, Andrea Bonetti, Adam Shmuel Teman

Among the different types of DRAMs, gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power and CMOS-compatible alternative to conventional SRAM. GC-eDRAM achieves high memory density as it relies on a storage cell that can be implemented with as few as ...

IEEE2020

Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Marco Antonio Rios

Resistive switching memory technologies (RRAM) are seen by most of the scientific community as an enabler for Edge-level applications such as embedded deep Learning, AI or signal processing of audio and video signals. However, going beyond a "simple'' repl ...

IEEE2020

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

Adaptive partitioning and indexing for in situ query processing

Anastasia Ailamaki, Ioannis Alagiannis, Manolis Karpathiotakis, Manoussos Gavriil Athanassoulis, Matthaios Alexandros Olma

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in ...

SPRINGER2020

Gain-Cell Embedded DRAMs: Modeling and Design Space

Andreas Peter Burg, Robert Giterman, Andrea Bonetti, Adam Shmuel Teman

Among the different types of dynamic random-access memories (DRAMs), gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power, and CMOS-compatible alternative to conventional static random-access memory (SRAM). GC-eDRAM achieves high memory density, as i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Aerolysin nanopores decode digital information stored in tailored macromolecular analytes

Aleksandra Radenovic, Matteo Dal Peraro, Chan Cao, Lucien Fabrice Krapp

Digital data storage is a growing need for our society and finding alternative solutions than those based on silicon or magnetic tapes is a challenge in the era of “big data.” The recent development of polymers that can store information at the molecular l ...

2020

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

David Atienza Alonso, Amir Aminifar, Miguel Peon Quiros, Adriana Arza Valdes, Elisabetta De Giovanni

In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel compu ...

2021