ERC Project

“Projestor”
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 era of 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.

Team

Abu Sebastian

Dr. Abu Sebastian
Principal Investigator

Geethan Karunaratne

Geethan Karunaratne

Riduan Khaddam-Aljameh

Riduan Khaddam-Aljameh

Relevant publications (selected)

[1] C. Rios, N. Youngblood, Z. Cheng, M. Le Gallo, W.H.P. Pernice, C.D. Wright, A. Sebastian, H. Bhaskaran,
In-memory computing on a photonic platform,”
Science Advances, 2019 (open access).

[2] A. Sebastian, M. Le Gallo, W.W. Koelmans, N. Papandreou, H. Pozidis, E. Eleftheriou,
Multi-level storage in phase-change memory devices,”
IBM RZ 3947, 2019.

[3] M. Le Gallo, A. Sebastian, G. Cherubini, H. Giefers, E. Eleftheriou,
Compressed Sensing With Approximate Message Passing Using In-Memory Computing,”
IEEE Trans. Electr. Dev.
65(10), 2018 (open access) IBM RZ 3944.

[4] I. Boybat, M. Le Gallo, S.R. Nandakumar, T. Moraitis, T. Parnell, T. Tuma, B. Rajendran, Y. Leblebici, A. Sebastian, E. Eleftheriou,
Neuromorphic computing with multi-memristive synapses,”
Nature Communications 9, 2514, 2018 (open access), PDF.

[5] A. Sebastian, M. Le Gallo, G.W. Burr, S. Kim, M. BrightSky, E. Eleftheriou,
Tutorial: Brain-inspired computing using phase-change memory devices,”
J. Appl. Phys. 124, 111101, 2018 (open access) IBM RZ 3946.

[6] I. Giannopoulos, A. Sebastian, M. Le Gallo, V.P. Jonnalagadda, M. Sousa, M.N. Boon, E. Eleftheriou,
8-bit Precision In-Memory Multiplication with Projected Phase-Change Memory,”
Proc. IEDM, 2018 (not yet open due to embargo period).

[7] 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)
PDF (open access).

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

[9] 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
arXiv preprint arXiv:1701.04279 (open access) PDF.

[10] S.R. Nandakumar, M. Le Gallo, I. Boybat, B. Rajendran, A. Sebastian, E. Eleftheriou,
Mixed-precision training of deep neural networks using computational memory”,
arXiv preprint arXiv:1712.01192, 2017 (open access).

[11] 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, PDF.

[12] 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.

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

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

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

[16] 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.

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

Research in the news

In-memory computing using photonic memory devices,” IBM Research Blog, 15 Feb 2019.

New possibilities in artificial intelligence,” issuu, 8 Jan 2019.

IBM reveals 8-bit analog chip with phase-change memory,” IEEE Spectrum, 3 Dec 2018.

Dual 8-bit breakthroughs bring AI to the edge,” IBM Research Blog, 3 Dec 2018.

Keep it simple: Towards single-elemental phase-change memory,” IBM Research Blog, 24 Jul 2018.

Novel synaptic architecture for brain inspired computing,” IBM Research Blog, 6 Jul 2018.

Glassy antimony makes monatomic phase change memory,” Physics World, 25 Jun 2018.

IBM's new in-memory computing solution makes business AI training faster, easier,” TechRepublic, 18 Apr 2018.

IBM: Our in-memory computing breakthrough will cut cost of training AI,” ZDNet, 18 Apr 2018.

IBM scientists demonstrate mixed-precision in-memory computing for the first time; Hybrid design for AI hardware,” IBM Research Blog, 17 Apr 2018.

Want A 200× Faster and More Energy Efficient Computer? IBM Research Develops Just The Tech,” disruptive, 5 Nov 2017.

IBM’s Phase Change Memory computer can tell you if it’s raining,” The Register, 31 Oct 2017.

IBM Demonstrates In-memory Computing with 1M PCM Devices,” HPC Wire, 30 Oct 2017.

IBM can run an experimental AI in memory, not on processors,” MIT Tech Review, 24 Oct 2017.

Computational Memory,” IBM Research Blog, 24 Oct 2017.

IBM scientists demonstrate in-memory computing with 1 million devices for applications in AI,” Phys.org, 24 Oct 2017.

Keralite in team signalling in-memory computing,” Deccan Chronicle, 17 Oct 2017.

IBM scientist Abu Sebastian develops future memory and computer paradigms with prestigious European grant,” IBM Research Blog, 20 March 2016.