949 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR485 papers
- Université Paris-SaclayFR482 papers
- Université Paris-SudFR188 papers
- Université Paris CitéFR87 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR71 papers
- Sorbonne UniversitéFR62 papers
- Université Grenoble AlpesFR47 papers
- Laboratoire Albert FertFR35 papers
- Institut NéelFR34 papers
- CEA GrenobleFR33 papers
- Laboratoire de physique des SolidesFR33 papers
- CEA Paris-SaclayFR31 papers
14 papers · 1 filter
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
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…
Image classification using collective optical modes of an array of nanolasers
Giulio Tirabassi, Ji Kaiwen, Cristina Masoller +1
Recent advancements in nanolaser design and manufacturing open up unprecedented perspectives in terms of high integration densities and ultra-low power consumption, making these de…
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…
Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse
Axel Laborieux, Marc Bocquet, Tifenn Hirtzlin +6
The design of systems implementing low precision neural networks with emerging memories such as resistive random access memory (RRAM) is a major lead for reducing the energy consum…
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…
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…
Neuromorphic Computing through Time-Multiplexing with a Spin-Torque Nano-Oscillator
M. Riou, F. Abreu Araujo, J. Torrejon +11
Fabricating powerful neuromorphic chips the size of a thumb requires miniaturizing their basic units: synapses and neurons. The challenge for neurons is to scale them down to submi…
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…
Use of Magnetoresistive Random-Access Memory as Approximate Memory for Training Neural Networks
Nicolas Locatelli, Adrien F. Vincent, Damien Querlioz
Hardware neural networks that implement synaptic weights with embedded non-volatile memory, such as spin torque memory (ST-MRAM), are a major lead for low energy artificial intelli…
Overcoming device unreliability with continuous learning in a population coding based computing system
Alice Mizrahi, Julie Grollier, Damien Querlioz +1
The brain, which uses redundancy and continuous learning to overcome the unreliability of its components, provides a promising path to building computing systems that are robust to…
A Neural Network Based on Synchronized Pairs of Nano-Oscillators
Damir Vodenicarevic, Nicolas Locatelli, Damien Querlioz
Artificial neural networks are intensively used to perform cognitive tasks such as image classification on traditional computers. With the end of CMOS scaling and increasing demand…
Neuromorphic computing with nanoscale spintronic oscillators
Jacob Torrejon, Mathieu Riou, Flavio Abreu Araujo +10
Neurons in the brain behave as non-linear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behavior to realize high de…