Chair of Nanoelectronics, TU Dresden
NaMLab gGmbH, Dresden
Chair for Fundamentals of Electrical Engineering, TU Dresden
Eter Mgeladze; Melanie Herzig; Richard Schroedter; Ronald Tetzlaff; Thomas Mikolajick; Stefan Slesazeck
2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 24-26 October 2022, Glasgow, United Kingdom
DOI: 10.1109/ICECS202256217.2022.9970915
Richard Schroedter; Ahmet Samil Demirkol; Alon Ascoli; Ronald Tetzlaff; Eter Mgeladze; Melanie Herzig; Stefan Slesazeck; Thomas Mikolajick
2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST), 08-10 June 2022, Bremen, Germany
Richard Schroedter; Eter Mgeladze; Melanie Herzig; Alon Ascoli; Stefan Slesazeck; Thomas Mikolajick; Ronald Tetzlaff
2022 IEEE International Symposium on Circuits and Systems (ISCAS), 27 May 2022 - 01 June 2022, Austin, TX, USA
The crossbar array, which realizes the matrix-vector multiplication (MVM) operation directly employing the Ohm law, represents the central building block of the non-conventional computing-in-memory (CIM) architectures, such as artificial neural networks (ANNs). The MVM is performed in an analog fashion, and, therefore, it is highly desirable to adopt non-volatile analog switching memristors, leveraging, furthermore, their memcapacitive properties.
In this project we aim at the exploitation of the interesting combined memristive and memcapacitive effects in Al2O3 / Nb2O3 based bi-layer device structures, recently developed at NaMLab to be used in conjunction with NFET transistors established at IHM. Based on these devices, we would like to develop a complete design methodology for the implementation of non-conventional versatile computations on a memristive/memcapacitive hardware, which utilizes a hybrid Memristor/Memcapacitor-CMOS architecture, mainly including MVM cores.
For this purpose, the proposed work will cover several essential aspects: (i) realization of cells integrating memristive/memcapacitive devices and NFETs, (ii) experimental and theoretical modelling of the single cells, (iii) system-level modelling, analysis, design, and simulation of the MVM core-based ANNs, (iv) derivation of optimization algorithms, that account for the physical limitations of the hardware realization, (v) set-up of efficient instruction sets and peripheral circuitry design, and (vi) optimized mapping of memcomputing tasks onto the ANN structures. In this way, we expect to gain a comprehensive understanding of the feasibility of adopting the aforementioned device concepts in unconventional computing systems, such as multi-layer perceptrons (MLP), convolutional neural networks (CoNN), and recurrent neural networks (ReNN). Finally, we will investigate the possibility of implementing the Quadratic-Integrate-and-Fire (QIF) neuron model in hardware, and to demonstrate the richness of biomimetic dynamical phenomena, which an array of artificial neural structures of this kind may support.
NaMLab gGmbH, TU Dresden
Chair of Nanoelectronics, TU Dresden
Chair of Fundamentals of Electrical Engineering, TU Dresden
Chair of Fundamentals of Electrical Engineering, TU Dresden
Chair of Fundamentals of Electrical Engineering, TU Dresden
Chair of Nanoelectronics, TU Dresden