Damien Querlioz

Bioinspired Nanolectronics

CNRS Researcher at Centre de Nanosciences et de Nanotechnologies

Recent News (updated 2/2019)

  • I am hiring a characterization-oriented postdoc for the NANOINFER project ! This position is ideal if you have experience in device and/or circuits characterization, and what to work on neuromorphics or Artificial Intelligence.
  • Our joint work with Aix-Marseille Univ and LETI on in-memory implementation of Binarized Neural Networks published at the IEDM conference! Link.
  • Our joint work with the groups of Julie Grollier (CNRS/Thales) and Shinji Yuasa (AIST), which demonstrates neuromorphic computation with coupled spin oscillators, published in Nature! Link.
  • Bogdan started as a postdoc in the group, and Axel as a PhD student!
  • Damir's new paper: adapting machine learning approaches for nanooscilator networks. Link. Featured on scilight
  • New paper with CNRS/Thales and NIST, USA. Making nanoelectronics-based neural network more resilient with continuous learning. Link.
  • New paper with LETI. In some RRAM-based neural networks, device variability is actually useful! Link.
  • I will be teaching again my graduate level class about "Nanoarchitecures" (circuit and system architectures that use nanodevices) from Dec to Feb for the Nanosciences and Integrated Circuits Master programs. The class is also open to PhD students. Contact me if you would like to attend.
  • Adrien's paper received the 2018 IEEE Biomedical Circuits and Systems Best Paper Award !
  • Alice and Tifenn's paper on population coding with superparamagnetic tunnel junctions accepted by Nature Communications! Link. Press release
  • Former News

Short Bio

Damien Querlioz is a tenured CNRS Researcher at the Centre de Nanosciences et de Nanotechnologies of Université Paris-Sud. He was first trained at Ecole Normale Supérieure Paris as a physicist and at Ecole Supérieure d'Electricité as a microelectronics engineer. He received the Ph.D. degree from Université Paris-Sud in 2009. He was then a Postdoctoral Scholar at Stanford University and at the Commissariat a l'Energie Atomique.

Damien Querlioz develops new concepts in nanoelectronics relying on bio-inspiration. He believes that nanoelectronics can allow us to invent highly energy-efficient forms of memory-centric computing. He investigates stochastic approaches, the use of spintronics devices for bioinspired systems, and the connection between bioinspired memories and Bayesian inference. His research interests have also included the physics of advanced nanodevices. He has developed the Wigner Monte Carlo approach to simulate and understand quantum transport in nanometer-scale devices.

In 2016, Damien Querlioz received the Habilitation degree (HDR). Since 2017, he is the coordinator of the interdisciplinary INTEGNANO research group, with amazing colleagues working around an integrative and multidisciplinary approach to the development of novel charge and spin based devices, as well as fantastic students and postdocs.

Damien Querlioz is a member of the bureau of GDR Biocomp, a French network to facilitate interdisciplinary exchanges around the realization of bio-inspired hardware systems, and a management committee member of the MEMOCIS COST action, a European-wide scientific and technology knowledge platform on memristive technology. He has coauthored 4 book chapters, more than 100 journal articles and conference proceedings and given more than 50 invited talks at national and international workshops and conferences. He has also coauthored the book "The Wigner Monte-Carlo Method for Nanoelectronic Devices" (London: ISTE; Hoboken: Wiley, 2010) with Philippe Dollfus. In 2017, he received a CNRS Bronze medal. He has also been a co-recipient of the 2017 IEEE Guillemin-Cauer Best Paper Award and of the 2018 IEEE Biomedical Circuits and Systems Best Paper Award.

The research of Damien Querlioz has been funded by the Seventh Framework Programme of the European Union (FETOPEN BAMBI), Agence Nationale de la Recherche, Région Ile-de-France/DIM NANO-K and Ministère de l'écologie, du développement durable et de l'énergie. In recent years, he also received funding from CNRS/Mission pour l'Interdisciplinarité and CNRS/INSIS. Starting March 2017, he has been leading the NANOINFER project, funded by a European Research Council Starting Grant for a duration of 5 years.


The progress of Artificial Intelligence is transforming the landscape of computing. But this comes at a considerable cost: energy. Computers and graphics cards were not developed with cognitive algorithms in mind. Brains, by contrast, have evolved to address specifically these tasks under extremely severe energy constraints. Therefore, I believe that brain-inspired paradigms can allow us to perform Artificial Intelligence in a much more energy efficient manner. And turns out, emerging nanotechnologies allow us to do precisely that!

My research is organized along two fundamental insights:

1. Brains do not separate computation and memory

Computers and graphics card rely on evolutions of the von Neumann paradigm: computation units and memory are separated, conceptually and physically. When they perform artificial intelligence tasks, more energy is used to move data between computation units and memory than to actually compute! By contrast brains limit considerably this data movement by using synapses as long term memory, which is located physically right where it needs to be used. This is possible because the “algorithms” used by the brain have a natural topology that do not require data movement.

Reproducing this idea with electronics is however complicated: we need to use static random access memory, which is very large, and volatile. Interestingly, nanotechnologies are now providing new classes of resistive memories that resemble brains’ synapses and can be put right in the middle of CMOS logic! In my research, I explore how we can use these devices in circuits specialized for cognitive algorithms and where computation and memory are entirely merged. More precisely, I am working on different flavors of neural networks and of Bayesian inference.

Recent papers: Look at this recent review on using nanosynapses, at this small scale experimental demonstration withorganic nanosynapses, at how we can use spin torque magnetic memory as stochastic synapses, and at these electrochemical synapses that look very much like real synapses.

2. Brains compute in an approximate fashion, by exploiting device physics

Computers and graphics card compute in an extremely accurate manner using Floating Point arithmetics. To achieve perfect determinism, the physics of their basic devices is highly abstracted: an object as complex as a transistor is used as a switch.

Turns out, the precision of computers is not needed for cognitive algorithms. The brain, by contrast computes in an approximate fashion, using extensively the physics of its nanodevices like ion channels and synapses.

Current development in nanoelectronics provide us with nanodevices that have very rich physics, which we would love to use for energy-efficient computation. They would be too imprecise for conventional algorithms, but they can be perfect for cognitive algorithms!

Recent papers: Look here how a spin torque nanooscillator can be used a neuron that performs spoken digit recognition. A more theoretical work on nanooscillators used as neurons. Superparamagnetic tunnel junctions can provide very interesting stochastic behaviors (here and here), and use of stochastic computing for cognitive tasks.

All this work is highly interdisciplinary and can only happen within a context of strong collaboration, and I am happy to work with an ensemble of fantastic collaborators!

Recent external collaborations: Julie Grollier at the CNRS/Thales lab (Palaiseau), Fabien Alibart at IEMN (Lille), Jacques Droulez and Pierre Bessière at ISIR (Paris), Emmanuel Mazer at LIG (Grenoble), Raphael Laurent at La Poste/ProBAYES (Grenoble), Elisa Vianello at LETI (Grenoble), Jean-Michel Portal and Marc Bocquet at IM2NP (Marseille), Sandip Tiwari at Cornell University (USA), Manan Suri at IIT Delhi (India), Omid Kavehei at the University of Sydney (Australia), Jayasimha Atulasimha at Virginia Commonwealth University (USA).

Contact Information

Email: damien.querlioz@u-psud.fr

Office Phone: (+33)170270403

Postal Address: Damien Querlioz, Centre de Nanosciences et de Nanotechnologies, 10 Boulevard Thomas Gobert, 91120 Palaiseau, France (new address!)

I am part of the the interdisciplinary INTEGNANO research group at C2N.

Group Visitors

I love to have visitors in the lab! Do not hesitate to contact if you would like to visit. Here are some former visitors.

Externally Funded Projects

Current Projects

  • ERC Starting Grant NANOINFER (2017-2022).
  • ANR BIOICE (2018-2022)
    • Coordinated by Julie Grollier. I represent the C2N partner.
    • The project investigates neuromorphic computation with spin ice
  • ANR MEMOS (2014-2018).
    • Coordinated by Julie Grollier. I represent the C2N partner.
      • The project investigates alternative model of computations using spin oscillators. With UMR CNRS/Thales, IEF/NOMADE and CEA/DSM.
  • Ministère de l'écologie, du développement durable et de l'énergie (2016-2018).
    • Coordinated by Damien Querlioz. Project "S'inspirer de la nature : une solution pour inventer des systèmes plus efficaces énergétiquement". Covers all the research costs of PhD student D. Vodenicarevic, and organization of a workshop with doctoral school EOBE, Univ. Paris-Saclay.

Past Projects

  • ANR JCJC CogniSpin (2013-2016) (young researcher grant).
    • Coordinated by Damien Querlioz. We used magnetic memory as nanosynapses. The project covered the device as well as the circuits and systems implications of this idea.
  • Région Ile-de-France / DIM NANO-K StochaChips (2013-2016).
    • Funded the PhD of Alice Mizrahi (joint PhD between UMR CNRS/Thales and IEF).
  • FP7 FETOPEN Bottom-up Approaches to Machines dedicated to Bayesian Inference (BAMBI 2014-2016)
    • Coordinated by Jacques Droulez at ISIR∕UPMC. I represent the IEF partner.
      • We proposed a theory and a hardware implementation of probabilistic computation inspired by biochemical cell signalling. We studied probabilistic computation following three axes: algebra, biology, and hardware.
      • With different CNRS labs (ISIR, UMR CNRS/Thales, LIG), Instituto de Sistemas e Robotica-Associacao (Portugal), the Hebrew University of Jerusalem (Israel), ProBayes SAS (France), Universite de Liege (Belgium).
  • CNRS/MI DEFIBAYES (2013 - 2014).
    • Coordinated by Damien Querlioz. We aimed at understanding how to design a Bayesian accelerator, from the nanotechnology to its macrosopic application. We focused specifically on the transition between levels from the ato- to the macro- scale (nanotechnology, circuit, architecture, programming, application). With UMR CNRS/Thales, TIMA, LPPA/ISIR, LIG. This project led to the FP7 FETOPEN BAMBI grant.
  • CNRS/MI Probabilistic Computing (2012).
    • Coordinated by Julie Grollier. With UMR CNRS/Thales, LPPA/ISIR, LIG. We discovered how spintronics could allow a novel form of Bayesian computing (BAMBI and DEFIBAYES projects).
  • CNRS/INSIS PEPS Stochastic Synapses (2011-2012).
    • Coordinated by Damien Querlioz. With UMR CNRS/Thales, IM2NP, CEA/DACLE. We discovered how nanosynapses used in a stochastic way could allow low-power cognitive-types problems..