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CogniGron Seminar: Sabina Spiga (CNR-IMM, Agrate Brianza Unit, Italy) - "Resistance switching memories for spiking neural networks"

When:Th 27-02-2020 11:00 - 12:00
Where:Energy Academy Europe (5159.291)

Memristive device technologies have been receiving an increasing interest for a wide range of applications, such as storage class memory, non-volatile logic switch, in-memory and neuromorphic computing. Among the proposed devices, oxide-based resistance switching memories (RRAM) are based on redox reactions and electrochemical phenomena in oxides and are very promising because of low power consumption, fast switching times, scalability down to nm scale and CMOS compatibility. Therefore, they can be used as new building blocks for brain-inspired computing technologies towards, for instance, spiking neural networks (SNN) for real time and low power computation systems. In this seminar, after a brief introduction of the current emerging technologies and devices for neuro-inspired architectures, I will present our work which span from materials and devices to system level simulation of SNN, including RRAM as key elements of synaptic nodes. In particular, it is possible to tune the RRAM conductance either in a digital or in an analog fashion by using various programming schemes, and the update of the conductance values can be achieved by a spike timing and rate dependent plasticity mechanism, which is demonstrated also at hardware level. System level simulation of SNN is implemented by using experimental RRAM characteristics for the synaptic nodes and neural equations derived from hardware CMOS implementation [1-3].

[1] J. Frascaroli, S. Brivio, E. Covi, and S. Spiga, Evidence of soft bound behaviour in analogue memristive devices for neuromorphic computing, Scientific Reports Vol. 8, 7178 (2018)

[2] E. Covi, R. George, J. Frascaroli, S. Brivio, C. Mayr, H. Mostafa, G. Indiveri and S. Spiga, Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons, Journal of Physics D: Applied Physics Vol. 51 Page 344003 (2018)

[3] S. Brivio, D. Conti, M. V. Nair, J. Frascaroli, E. Covi, C. Ricciardi, G. Indiveri, and S. Spiga, Extended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics, Nanotechnology 30, 015102 (2019)

More about Sabina
Sabina Spiga is Senior Researcher at CNR-IMM – Unit of Agrate Brianza, Italy. She received the Degree in Physics from the Università di Bologna in 1995 and the PhD in Material Science in 2002 from Università di Milano. She is currently in charge of developing oxide-based resistive switching non-volatile memories and memristive devices for brain-inspired computation systems. She has been PI for CNR of the EU project- NeuRAM3- “NEUral computing aRchitectures in Advanced Monolithic 3D-VLSI nano-technologies”, and she is currently PI of the Horizon-2020 projects NEUROTECH -“Neuromorphic Computing Technologies” and MeM-Scales “Memory technologies with multi-scale time constants for neuromorphic architectures”. She is currently member of the IEDM Memory Technology subcommittee (2019-2020) and of editorial board of the J. Phys D: Applied Physics.

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