Abstract: When performing artificial intelligence tasks, central and graphics processing units consume considerably more energy for moving data between logic and memory units than for doing actual arithmetic. Brains, by contrast, achieve vastly superior energy efficiency by fusing logic and memory entirely, performing a form of "inBmemory" computing. Currently emerging memory nanodevices such as (mem)resistive, phase change and magnetic memories give us an opportunity to achieve similar tight integration between logic and memory. In this talk, we will look at neuroscience inspiration to extract lessons on the design of inBmemory computing systems. We will first study the reliance of brains on approximate memory strategies, which can be reproduced for artificial intelligence. We will give the example of a hardware binarized neural network relying on resistive memory. Binarized neural networks are a class of deep neural networks discovered in 2016, which can achieve stateB ofBtheBart performance with a highly reduced memory and logic footprint with regards to conventional artificial intelligence approaches. Based on measurements on a hybrid CMOS and resistive Hafnium oxide memory chip exploiting a differential approach, we will see that such systems can exploit the properties of emerging memories without the need of error correcting codes, and achieve extremely high energy efficiency. Second, we will see that brains use the physics of their memory devices in a way that is much richer than only storage. This can inspire radical electronic designs, where memory devices become a core part of computing. We will illustrate this concept by our works using magnetic memories as artificial neurons. We have fabricated neural networks where magnetic memories used as nonlinear oscillators implement neurons, and their electrical couplings implement synapses. We will see that such designs can harness the rich physics and dynamics inherent to magnetic memories, without suffering from their drawbacks. This physicsBrich approach nevertheless raises important challenges that we will highlight.

Bio: Damien Querlioz is a CNRS Researcher at the Centre de Nanosciences et de Nanotechnologies of Université ParisBSud. He focuses on novel usages of emerging nonBvolatile memory and other nanodevices, in particular relying on inspirations from biology and machine learning. He received his predoctoral education at Ecole Normale Supérieure, Paris and his PhD from Université ParisBSud in 2009. Before his appointment at CNRS, he was a Postdoctoral Scholar at Stanford University and at the Commissariat a l'Energie Atomique. Damien Querlioz is the coordinator of the interdisciplinary INTEGNANO research group, with colleagues working on all aspects nanodevice physics and technology, from materials to systems. He is a member of the bureau of the French Biocomp research network, and a management committee member of the European MEMOCIS COST action. He has coauthored one book, four book chapters, more than 100 journal articles and conference proceedings, and given more than 50 invited talks at national and international workshops and conferences. In 2016, he was the recipient of an ERC Starting Grant to develop the concept of natively intelligent memory. In 2017, he received the CNRS Bronze medal. He has also been a coBrecipient of the 2017 IEEE GuilleminBCauer Best Paper Award and of the 2018 IEEE Biomedical Circuits and Systems Best Paper Award.