Future Computing Strategies in Nano-Electronic Systems

Modul NES-12 08 01-20.1

Lecturer

n.n.

Course Description

With CMOS scaling approaching atomic boundaries, keeping the integrated circuit (IC)
performance growth rate predicted by Moore’s law [1] in the years to come relies on the
introduction of novel nanotechnologies, which may enable the hardware implementation of innovative forms of computing, allowing to foster progress in electronics without shrinking transistor dimensions any further.

In this regard, the disruptive memristive technologies [2]-[3] may open up a wide range of opportunities in future IC design, especially in view of the extraordinary and unique capability of non-volatile resistance switching memories to process signals and store data within the same physical nanoscale volume. This unique feature is the key to the development of non-von Neumann mem-computing machines [4], and to the realisation of miniaturised, lightweight, high-speed, and low-power technical systems [5], able to process, and store information in the same location where data acquisition takes place. The theoretical foundations of future processing paradigms as well as the operating principles of their circuit implementations will be comprehensively discussed, and complemented with numerical examples, offering the students a truly-pedagogical introduction to the state-of-the-art in future computing strategies in nano-electronic systems.

The course will cover various paradigms, including neuromorphic computing [6], where the Theory of Local Activity and Edge of Chaos [7]- [8] assume a primary role, crosspoint crossbar computing [9], neural network computing [10], and quantum computing [11], which stand at the forefront of state-of-the-art research in both academics and industry, and promise to endow future machines with unprecedented signal processing capability.

References

[1] R.S. Williams, “What's next? [The end of Moore's law],” IEEE Computing in Science &
Engineering, vol. 19, no. 2, pp. 7{13, 2017, DOI: 10.1109/MCSE.2017.31
[2] L.O. Chua, “Everything You Wish to Know About Memristors But Are Afraid to Ask,”
Radioengineering, vol. 24, no. 2, pp. 319{368, June 2015, DOI: 10.13164/re.2015.0319
[3] R. Waser, and M. Aono, “Nanoionics-based resistive switching memories,” Nature Materials, vol. 6, no. 11, pp. 833-840, 2007
[4] M. Di Ventra, and F.L. Traversa, “Perspective: Memcomputing: Leveraging memory and physics to compute efficiently,” Journal of Applied Physics, 123, 180901(15pp.), 2018
[5] A. Rodríguez-Vázquez, J. Fernández-Berni, J.A. Leñero-Bardallo, I. Vornicu, and R. Carmona-Galán, “CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends,” IEEE Circuits and Systems Magazine, vol. 18, no. 2, pp. 90-107, 2018
[6] W. Yi, K.K. Tsang, S.K. Lam, X. Bai, J.A. Crowell, and E.A. Flores, “Biological plausibility and stochasticity in scalable VO2 active memristor neurons,” Nature Communications, vol. 9, no. 4661, pp. 1-10, 2018, DOI: 10.1038/s41467-018-07052-w
[7] K. Mainzer, and L.O. Chua (2013) The Local Activity Principle, Imperial College Press, ISBN: 978-1908977090
[8] L.O. Chua, “Local activity is the origin of complexity,” Int. J. Bifurc. Chaos, vol. 15, no. 11, pp. 3435-3456, 2005
[9] Z. Sun, G. Pedretti, E. Ambrosi, A. Bricalli, W. Wang, and D. Ielmini, “Solving matrix equations in one step with cross-point resistive arrays,” Proceedings of the National Academy of Sciences of the United States of America (PNAS), vol. 116, no. 10, pp. 4123-4128, 2019
[10] A. Ascoli, R. Tetzlaff, Sung-Mo (Steve) Kang, and L.O. Chua, “Theoretical Foundations of Memristor Cellular Nonlinear Networks: A DRM2-based Method to Design Memcomputers with Dynamic Memristors,” IEEE Trans. on Circuits and Systems–I: Regular Papers, 2020, DOI: 10.1109/TCSI.2020.2978460
[11] P. Pfeiffer, I.L. Egusquiza, M. Di Ventra, M. Sanz, and E. Solano, “Quantum Memristors,” Scientific Reports, vol. 6, 29507, 2016, DOI: 10.1038/srep29507

Course Contents

• Memristive Devices, Circuits, and Systems
  • Classes of Memory Resistors
  • Theory of Memristors
  • Nonlinear Dynamics of Memristors
  • Application of Memristors
• Signal Processing Paradigms Enabled by Disruptive Memristive Nanotechnologies
  • Neuromorphic Computing
  • Crosspoint Crossbar Computing
  • Neural Network Computing
  • Quantum Computing

Links

• OPAL courses