Networks of stochastic magnetoresistive components for ultra-low-power cognitive computing

The automated resolution of cognitive tasks primarily relies on learning algorithms applied to neural networks which, when executed on standard architectures, lead to a power consumption several orders of magnitude larger than what the brain would require. This consumption can be drastically reduced by using hardware computing systems with an architecture inspired by biological or physical models, based on nanodevices mimicking the properties of neurons such as the emission of stochastic or synchronous spikes.

Numerous theoretical proposals have shown that nanoscale and multifunctional magnetic tunnel junctions (MTJ) are particularly adapted. The next step is to have a large number of such components interacting on-chip via reconfigurable coupling weights, and demonstrate experimentally an advantage on standard cognitive tasks, both in terms of performance and energy consumption.

The proposed research project shall lead to the prototyping and functional validation of an integrated test vehicle with CMOS and stochastic MTJs. The candidate will be placed at the interface between the nanofabrication and design teams. This will require a familiarity with the basics of the computational algorithmics (role of topology and stochasticity in improving the performance/consumption trade-off), as well as the physics of the magnetic nanodevices (impact of the technology stack on magnetic and electrical characteristics). The work will consist in the electrical characterization and the simulation of the CMOS/MTJ building blocks, in order to bridge the gap between system-level specifications and fabrication constraints.

We are looking for outstanding applicants with a strong background in the physics of electronic and magnetic components, as well as nanofabrication and electrical characterization techniques. Basic knowledge in neuromorphic computing is an advantage. A genuine enthusiasm for scientific research and the ability to communicate in a pluridisciplinary environment will be highly valued.

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