Memory technologies with multi-scale time constants for neuromorphic architectures

Projektinformationen

MeM-Scales

ID Finanzhilfevereinbarung: 871371

DOI

10.3030/871371

Projekt abgeschlossen

EK-Unterschriftsdatum 5 Dezember 2019

Startdatum 1 Januar 2020

Enddatum 30 Juni 2023

Finanziert unter

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

Gesamtkosten

€ 3 950 628,75

EU-Beitrag

€ 3 950 628,75

Koordiniert durch

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)

Berichtszeitraum: 2022-01-01 bis 2023-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

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).

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

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.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

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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