Memory is at the center of modern electronics. However, the way that it is used is far from optimal: considerable energy is wasted due to the basic design of computers that separates computation elements and memory1. This PhD project aims at addressing this situation by designing “intelligent memories” that work much more like Human memory than computer memory. The key to achieve this goal lies at two levels. At the technology level, we will exploit novel technologies from the field of “spin electronics”2. At the architecture level, we will develop memory chips that can also reason.
This effort is part of a large project that recently received 1.5M€ of funding through the European Research Council (ERC) for the invention of natively intelligent memory.
Scientific and technical work, prerequisites:
During the internship and PhD thesis, the student will draw a parallel between spin electronics and models of reasoning (based on Bayesian theory) that can be used to implement intelligent memory. He/she will investigate how spintronic devices can store conditional probability distributions and implement an inference mechanism.
The internship will mostly be based on theoretical studies. The student will simulate design and simulate basic blocks for Bayesian inference. The methodology will be based on Matlab and a circuit-level simulator (Cadence).
The subsequent PhD work will associate a mix of theoretical investigations, experimental characterization of spin electronics devices, and fabrication of small prototypes, depending on the student’s interests and abilities. The PhD will be located at the Centre de Nanosciences et de Nanotechnologies (https://sites.google.com/site/damienquerlioz/), and include very substantial collaboration with the “Nanodevices for bio-inspired computing” group of Unité mixte de Physique CNRS/Thales (http://julie.grollier.free.fr/).
This challenging project is ideal for an engineering student, a physics student with strong interest in computing, or a computer science student with strong interest in physics and electronics. The knowledge of a programming language is mandatory. Curiosity and taste for learning new material from new fields is essential. Knowledge of Bayesian probability, of Cadence or of spin electronics is not expected before the internship.
1D. Querlioz et al, “Bioinspired Programming of Memory Devices for Implementing an Inference Engine“, Proceedings of the IEEE. 103, p. 1398, 2015 (2015)
2 J. Grollier, D. Querlioz, M. D. Stiles, “Spintronic nano-devices for bio-inspired computing”, Proceedings of the IEEE, Vol. 104, No. 10, p. 2024 (2016)
See also: http://nanotechweb.org/cws/article/tech/60960
Skills to be learnt: The student will learn about novel ideas for computing, spin electronics and new memory technology. He/she will learn several methodologies of simulation and experimental characterization. The PhD is adapted to a career in both academia and industry.
Funding: Funding for the internship and the PhD thesis is available through the ERC project.