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Neural processing in the nervous system occurs naturally over multiple time scales ranging from milliseconds (axonal transmission) to seconds (spoken phrases) and much longer intervals (motor learning).

In MeM-Scales we aim at building a novel class of neuromorphic computing systems that reproduce multi-timescale processing of biological neural systems, for building a novel class of neuromorphic computing systems that can process efficiently real-world sensory signals and natural time-series data in real-time (e.g. for low-power and always-on IoT and edge-computing applications that do not need to connect to the cloud), and to demonstrate this with a practical laboratory prototype.

The project consortium is broadly interdisciplinary, joining 9 international groups (Research centers, Universities and Industry) from material science through microchip design & manufacturing to computational neuroscience and machine learning.

Coordinator: CEA-Leti, France

Participants: IMEC, IMEC-NL, IBM, CNR-IMM, Consejo superior de Investigaciones Científicas (CSIC), University of Zurich, Univ. Groningen, aiCTX AG

Objectives.  Our scientific and technological objectives can be summarized as follows:

  1. To study the theory, and develop algorithmic and architectural innovations for realizing adaptive and robust multi-timescale neural processing on mixed-signal analog/digital neuromorphic processors comprising both volatile and non-volatile memory devices to implement the synaptic circuits and TFT-based neurons.
  2. To develop novel hardware technologies that support on-chip learning with multiple time constants, both for synapses (volatile memory option combined with non-volatile memory, Electrochemical metallization, vacancy-type oxide-based memories, and Phase Change Memory), and neurons (TFT option exploration, plus integration with other devices).
  3. To study and develop an ultra-low-power, scalable and highly configurable neuromorphic computing processor capable of online, life-long learning for personalized neural learning and adaptation algorithms.
  4. To validate and demonstrate the project developments on realistic fully personalized edge application cases (by both simulation and board prototyping).

CNR Principal Investigator: Sabina Spiga

Involved Key Personnel: Stefano Brivio

Post Docs: Mrinmoy Dutta


  1. Stefano Brivio, Denys R. B. Ly, Elisa Vianello and Sabina Spiga, Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks, Frontiers in Neuroscience 15, 580909 (2021); https://doi.org/10.3389/fnins.2021.580909


2021-02-08 to 2023-06-30