ERC Project

Projected Memristor

A nanoscale device for cognitive computing

Research objective

Resistive memory devices could have a significant influence on future computing systems.

—Abu Sebastian
ERC Grant recipient

We are entering the third era of computing: cognitive computing, which holds great promise in terms of deriving intelligence and knowledge from huge volumes of data. Today’s cognitive computers are based on the von Neumann architecture, in which the computing and the memory units are separated. Cognitive computing, however, is inherently data-centric, meaning that huge amounts of data need to be shuttled back and forth at high speeds, a task at which that architecture is highly inefficient.

It is becoming increasingly clear that to build efficient cognitive computers, we need to transition to non-von Neumann architectures where memory and logic coexist in some form. Brain-inspired neuromorphic computing and the fascinating new area of in-memory computing are two key non-von Neumann approaches being researched. The critical element in these novel computing paradigms is a very-high-density, low power, variable-state, programmable and non-volatile nanoscale memory device.

The goals of this project are to explore such a memory device concept, to develop computing architectures and algorithms and to explore application domains for emerging non-von Neumann computing paradigms.

Relevant publications (selected)

[1] M. Salinga, B. Kersting, I. Ronneberger, V.P. Jonnalagadda, X.T. Vu, M. Le Gallo, I. Giannopoulos, O. Cojocaru-Miredin, R. Mazzarello, A. Sebastian,
Monatomic phase change memory,”
Nature Materials, 17 681–685, 2018 (Cover).

[2] M. Le Gallo, D. Krebs, F. Zipolli, M. Salinga, A. Sebastian,
Collective structural relaxation in phase-change memory devices,”
Adv. Electronic Materials, 2018.

[3] M. Le Gallo, A. Sebastian, R. Mathis, M. Manica, H. Giefers, T. Tuma, C. Bekas, A. Curioni, E. Eleftheriou,
Mixed-precision in-memory computing,”
Nature Electronics 1, 246–253, 2018.

[4] A. Sebastian, T. Tuma, N. Papandreou, M. Le Gallo, L. Kull, T. Parnell, E. Eleftheriou,
Temporal correlation detection using computational phase-change memory,”
Nature Communications 8, 2017.

[5] G.W. Burr, R.M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M. Ishii, P. Narayanan, A. Fumarola, L.L. Sanches,
Neuromorphic computing using non-volatile memory,”
Advances in Physics: X. 2(1), 2017.

[6] T. Tuma, A. Pantazi, M. Le Gallo, A. Sebastian, E. Eleftheriou,
Stochastic phase-change neurons,”
Nature Nanotechnology 11(8),2017.

[7] T. Tuma, M. Le Gallo, A. Sebastian, E. Eleftheriou,
Detecting correlations using phase-change neurons and synapses,”
IEEE Electron Device Letters
37(9), 2016.

[8] W. Koelmans, A. Sebastian, V. P. Jonnalagadda, D. Krebs, L. Dellmann, E. Eleftheriou,
Projected phase-change memory devices,”
Nature Communications 6, 2015.

[9] P. Hosseini, A. Sebastian, N. Papandreou, C.D. Wright, H. Bhaskaran,
Accumulation-based computing using phase-change memories with FET access devices,”
IEEE Electron Device Letters, 36(9), 2015.

[10] A. Sebastian, M. Le Gallo, D. Krebs,
Crystal growth within a phase change memory cell,”
Nature Communications 5, 2014.