MemrisTec
Madison Cotteret, Hugh Greatorex, Alpha Renner, Junren Chen, Emre Neftci, Huaqiang Wu, Giacomo Indiveri, Martin Ziegler, Elisabetta Chicca
Madison Cotteret et al 2025 Neuromorph. Comput. Eng., 7 February 2025
Im Workshop MemrisTec2024 wurden Dimitris Spithouris, Johannes Hellwig und Hugh Greatorex für die beste Präsentation vom MemrisTec Board ausgezeichnet.
Johannes Hellwig, Carsten Funck, Sebastian Siegel, Alexandros Sarantopoulos, Dimitrios Spithouris, Stephan Menzel, Regina Dittmann
Advanced Electronic Materials, 17 June 2024
Johannes Hellwig from Forschungszentrum Jülich (PGI-7) received the “Best Oral Award” for his presentation “Resolving the Physical Origin of LRS Relaxation in Valence Change Memory” at the MEMRISYS conference in Turin, which took place from November 5-9, 2023.
Madison Cotteret, Hugh Greatorex, Martin Ziegler, Elisabetta Chicca
Neural Computation 36, 549–595 (2024), October 19 2023
DOI: 10.1162/neco_a_01638
Ole Richter, Hugh Greatorex, Benjamin Hucko, Madison Cotteret, Willian Soares Girao, Ella Janotte, Michele Mastella, Elisabetta Chicca
Synaptic Normalisation for On-Chip Learning in Analog CMOS Spiking Neural Networks. In International Conference on Neuromorphic Systems (ICONS ’23), August 1-3, 2023
Michele Mastella, Hugh Greatorex, Madison Cotteret, Ella Janotte, Willian Soares Girão, Ole Richter, Elisabetta Chicca.
Synaptic Normalisation for On-Chip Learning in Analog CMOS Spiking Neural Networks. In International Conference on Neuromorphic Systems (ICONS ’23), August 1–3, 2023
In the Internet of Things (IoT) era there is a growing amount of sensory data to be processed. IoT sensors often require the use of wireless communication at the cost of high power consumption. Sensors smart enough to compute data are needed to reduce the communication load and can offer the advantage of local decision making. While there are several advances in the field of sensors and sensor networks, the technology for complex processing at the sensing node is still to be developed, especially for applications requiring compact low-power systems operating with very low latencies.
In this project, we will empower a recently proposed computational element, namely the TDE, suitable for low-latency and low-power sensory information processing, with the advantages provided by a hybrid CMOS-memristive implementation. We will engineer volatile redox-based memristive devices with tailored decay times to replace the capacitor used in the CMOS implementation. This will guarantee compactness while enabling the achievement of long time constants (from hundred of milliseconds to seconds) prohibitive for analog CMOS. The combination of short (from microseconds to tens of milliseconds) and long time constants will further extend the field of application of the proposed computational module.
This project will take advantage from the synergies of two groups with strongly complementary expertise on memristive device development and analog circuit design.The device engineering and CMOS design efforts planned in this proposal will advance the state-of-the-art in memristive devices and hybrid CMOS-memristive systems. The strategy to engineer the decay times of memristive devices is based on elucidating the details of the redox-processes at the oxide-electrode interface, governing the time stability of the resistive states. The knowledge about the underlying physico-chemical processes causing the time decay will provide novel design rules for both volatile and non-volatile memristive devices in the future.
The demonstrator envisioned in this research will enable innovation in smart sensing. We will have the unique opportunity to explore a variety of sensory domains, including vision and audition and possibly touch and olfaction, therefore finding innovative solutions to open sensing problems.
