Brain-Inspired Computing Using Magnetic Domain Wall Devices : vTools Events

Neuromorphic computing or brain-inspired computing is considered as a potential solution to overcome the energy inefficiency of the von Neumann architecture for artificial intelligence applications [1]-[4]. In order to realize spin-based neuromorphic computing practically, it is essential to design and fabricate electronic analogues of neurons and synapses. An electronic analogue of a synaptic device should provide multiple resistance states. A neuron device should receive multiple inputs and should provide a pulse output when the summation of the multiple inputs exceeds a threshold.

We have been carrying out investigations on the design and development of various synaptic and neuron devices in our laboratory. Domain wall (DW) devices based on magnetic tunnel junctions (MTJs), where the DW can be moved by spin-orbit torque, are suitable candidates for the fabrication of synaptic and neuron devices [2]. Spin-orbit torque helps in achieving DW motion at low energies whereas the use of MTJs helps in translating DW position information into resistance levels (or voltage pulses) [3]. This talk will summarize various designs of synthetic neurons synaptic elements and materials [4]. The first half of the talk will be at an introductory level, aimed at first-year graduate students. The second half will provide details of the latest research.

[1] K. Roy, A Jaiswal, and P Panda, “Towards Spike-Based Machine Intelligence With Neuromorphic Computing,” Nature 575, 607-617 (2019).

[2] W. L. W. Mah, J. P. Chan, K. R. Ganesh, V. B. Naik, S. N. Piramanayagam, “Leakage Function in Magnetic Domain Wall Based Artificial Neuron Using Stray Field,” Appl. Phys. Lett. 123, 092401 (2023).

[3] D. Kumar, H. J. Chung, J. P. Chan, T. L. Jin, S. T. Lim, S. S. P. Parkin, R. Sbiaa, S. N. Piramanayagam, “Ultralow Energy Domain Wall Device for Spin-Based Neuromorphic Computing,” ACS Nano 17, 6261-6274 (2023).

[4] R. Maddu, D. Kumar, S. Bhatti, S. N. Piramanayagam, “Spintronic Heterostructures for Artificial Intelligence: A Materials Perspective,” Phys. Stat. Sol. RRL 17, 2200493 (2023).

S. N. (Prem) Piramanayagam got the Master’s degree in Physics from the University of Kerala, India, in 1988 and the Ph.D. from the Indian Institute of Technology, Bombay, India, in 1994. He carried out further research at Shinshu University, Japan (1995-1999) and worked in the Data Storage Institute (DSI), Singapore (A*STAR). He is currently an Associate Professor at Nanyang Technological University (NTU), Singapore. He has 30 years of experience in magnetism, with research topics including amorphous magnetic alloys, permanent magnetic materials, and thin films and nanostructures for recording and spintronics applications. His current interest lies in the interdisciplinary areas of magnetism, electronics, and nanotechnology.

Prem has received an award for teaching excellence from NTU Singapore and several awards for outstanding research from DSI Singapore. He is a Senior Member of IEEE and has been an active volunteer in the IEEE Magnetics Society, including chair of Technical Committee, elected member of the Administrative Committee, chair of the Singapore Chapter, and co-chair of the 2018 Intermag Conference in Singapore. He has published about 200 journal articles and has filed several patent applications. He serves as an editor of IEEE Transactions on Magnetics and as editor-in-chief of Nano (World Scientific). He co-edited the book, Developments in Data Storage: Materials Perspective (Wiley-IEEE Press, 2011).

IEEE Magnetics Society Position History:

Present Singapore Chair (Singapore)Present Editor, Transactions on Magnetics (IEEE Transactions on Magnetics Editorial Committee)Present Singapore Chair (Chapters Committee Roster)Present Editor, Transactions on Magnetics (Publications Committee Roster)Present IEEE Press Liaison (Publications Committee Roster)2021-2023 Elected Members (Voting) (Elected Members (Voting))

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2024-2024 Distinguished Lecturer

Brain-Inspired Computing Using Magnetic Domain Wall Devices

Brain-Inspired Computing Using Magnetic Domain Wall Devices

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Brain-Inspired Computing Using Magnetic Domain Wall Devices

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