582 citations · 768 across the 23 of their papers we have counts for
14 papers · 1 filter
Forward-only learning in memristor arrays with month-scale stability
Adrien Renaudineau, Mamadou Hawa Diallo, Théo Dupuis +12
Turning memristor arrays from efficient inference engines into systems capable of on-chip learning has proved difficult. Weight updates have a high energy cost and cause device wea…
The Logarithmic Memristor-Based Bayesian Machine
Clément Turck, Kamel-Eddine Harabi, Adrien Pontlevy +9
The demand for explainable and energy-efficient artificial intelligence (AI) systems for edge computing has led to significant interest in electronic systems dedicated to Bayesian…
Powering AI at the Edge: A Robust, Memristor-based Binarized Neural Network with Near-Memory Computing and Miniaturized Solar Cell
Fadi Jebali, Atreya Majumdar, Clément Turck +13
Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired…
A Multimode Hybrid Memristor-CMOS Prototyping Platform Supporting Digital and Analog Projects
Kamel-Eddine Harabi, Clement Turck, Marie Drouhin +7
We present an integrated circuit fabricated in a process co-integrating CMOS and hafnium-oxide memristor technology, which provides a prototyping platform for projects involving me…
Experimental demonstration of Single-Level and Multi-Level-Cell RRAM-based In-Memory Computing with up to 16 parallel operations
E. Esmanhotto, T. Hirtzlin, N. Castellani +7
Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device en…
A Memristor-Based Bayesian Machine
Kamel-Eddine Harabi, Tifenn Hirtzlin, Clément Turck +7
In recent years, a considerable research effort has shown the energy benefits of implementing neural networks with memristors or other emerging memory technologies. However, for ex…
Embracing the Unreliability of Memory Devices for Neuromorphic Computing
Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein +4
The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near- or in-memory. However, this type of computation is not compatible with c…
Implementation of Ternary Weights with Resistive RAM Using a Single Sense Operation per Synapse
Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +5
The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a significant lead for reducing the energy…
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein +5
One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow impr…
Digital Biologically Plausible Implementation of Binarized Neural Networks with Differential Hafnium Oxide Resistive Memory Arrays
Tifenn Hirtzlin, Marc Bocquet, Bogdan Penkovsky +5
The brain performs intelligent tasks with extremely low energy consumption. This work takes inspiration from two strategies used by the brain to achieve this energy efficiency: the…
Stochastic Computing for Hardware Implementation of Binarized Neural Networks
Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet +3
Binarized Neural Networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the re…
Outstanding Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Tifenn Hirtzlin, Marc Bocquet, Jacques-Olivier Klein +4
Resistive random access memories (RRAM) are novel nonvolatile memory technologies, which can be embedded at the core of CMOS, and which could be ideal for the in-memory implementat…
In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks
Marc Bocquet, Tifenn Hirztlin, Jacques-Olivier Klein +4
RRAM-based in-Memory Computing is an exciting road for implementing highly energy efficient neural networks. This vision is however challenged by RRAM variability, as the efficient…
Neural-like computing with populations of superparamagnetic basis functions
Alice Mizrahi, Tifenn Hirtzlin, Akio Fukushima +4
In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allo…