Memory technologies with multi-scale time constants for neuromorphic architectures

Project Information

MeM-Scales

Grant agreement ID: 871371

DOI

10.3030/871371

Project closed

EC signature date 5 December 2019

Start date 1 January 2020

End date 30 June 2023

Funded under

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Total cost

€ 3 950 628,75

EU contribution

€ 3 950 628,75

Coordinated by

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
France

Periodic Reporting for period 2 - MeM-Scales (Memory technologies with multi-scale time constants for neuromorphic architectures)

Reporting period: 2022-01-01 to 2023-06-30

Summary of the context and overall objectives of the project

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).

The main objective of the EU H2020 MeM-Scales project is the joint co-development of a novel class of algorithms, devices, and circuits 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.

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).

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Throughout the course of the project, we have designed, fabricated, and extensively tested multiple neural network chips employing diverse technologies including resistive memories, ferroelectric memories, and TFTs. The incorporation of synapse with tunable weights and delay elements directly into the network using resistive memories and ferroelectric devices has validated the initial concept. Furthermore, the project has demonstrated the remarkable utility of TFTs as multi-timescale neurons.
The project's progress has resulted in the publication of 26 articles, including one in Nature Electronics and three in Nature Communications.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The technologies designed in MeM-Scales target the IoT / edge computing markets, which is expected to grow sharply in the coming years. The market for edge computing chipsets is projected to surpass 50 billion USD by 2025; in particular, the market is projected to outstrip cloud computing chipsets (e.g. devices based in data centers) by a factor of three.
MeM-Scales is focused on event-based or spiking neural network (SNN) or spiking neuromorphic applications where a diverse range of time scales is present and required. So, we mostly aim at streaming applications where the input/sensor data is sampled in a near-continuous stream and information of the past has to be stored across multiple time horizons. One major application domain where this is valid is in autonomous navigation and moving vehicles such as robots, drones and even cars. In this-case, one can take advantage of heterogeneous collection of video cameras, radar sensors and potentially also lidars. Another major application target domain are sensor-based healthcare and life-style systems such as smart patches, smart wristbands, smart glasses and even smart shoes. Also in that case, we can make use of sensory fusion by combining a heterogeneous set of sensors for collecting information such as ECG, EMG, bio-impedance streams and potentially also brain signals through EEG sensors and neuro-probes.
Major progress beyond the state of the art is expected by merging all the innovations at the levels of algorithms, non-volatile / volatile devices, neuromorphic circuit designs, and scalable CMOS and TFT connection schemes in a cross-disciplinary effort towards the realization of multi-time scale spiking neural processing systems. MeM-Scales targets at deliver ultra-low power autonomous life-long on-line learning systems which will have a tremendous impact on the above mentioned edge computing applications domains.

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