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DESCRIPTION:Brain-Inspired Computing Using Magnetic Domain Wall Devices\n\n
 Prof. S.N. (Prem) Piramanayagam\n\nNanyang Technological University\n\nNeu
 romorphic computing or brain-inspired computing is considered as a potenti
 al solution to overcome the energy inefficiency of the von Neumann archite
 cture for artificial intelligence applications [1]-[4]. In order to realiz
 e spin-based neuromorphic computing practically\, it is essential to desig
 n and fabricate electronic analogues of neurons and synapses. An electroni
 c 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.\n\n
 We have been carrying out investigations on the design and development of 
 various synaptic and neuron devices in our laboratory. Domain wall (DW) de
 vices based on magnetic tunnel junctions (MTJs)\, where the DW can be move
 d by spin-orbit torque\, are suitable candidates for the fabrication of sy
 naptic and neuron devices [2]. Spin-orbit torque helps in achieving DW mot
 ion at low energies whereas the use of MTJs helps in translating DW positi
 on information into resistance levels (or voltage pulses) [3]. This talk w
 ill summarize various designs of synthetic neurons synaptic elements and m
 aterials [4]. The first half of the talk will be at an introductory level\
 , aimed at first-year graduate students. The second half will provide deta
 ils of the latest research.\n\n[1] K. Roy\, A Jaiswal\, and P Panda\, “T
 owards Spike-Based Machine Intelligence With Neuromorphic Computing\,” N
 ature 575\, 607-617 (2019).\n\n[2] W. L. W. Mah\, J. P. Chan\, K. R. Ganes
 h\, V. B. Naik\, S. N. Piramanayagam\, “Leakage Function in Magnetic Dom
 ain Wall Based Artificial Neuron Using Stray Field\,” Appl. Phys. Lett. 
 123\, 092401 (2023).\n\n[3] D. Kumar\, H. J. Chung\, J. P. Chan\, T. L. Ji
 n\, S. T. Lim\, S. S. P. Parkin\, R. Sbiaa\, S. N. Piramanayagam\, “Ultr
 alow Energy Domain Wall Device for Spin-Based Neuromorphic Computing\,” 
 ACS Nano 17\, 6261-6274 (2023).\n\n[4] R. Maddu\, D. Kumar\, S. Bhatti\, S
 . N. Piramanayagam\, “Spintronic Heterostructures for Artificial Intelli
 gence: A Materials Perspective\,” Phys. Stat. Sol. RRL 17\, 2200493 (202
 3).\n\nHönggerbergring \, ETH \, Zurich\, Switzerland\, Switzerland\, 809
 3
LOCATION:Hönggerbergring \, ETH \, Zurich\, Switzerland\, Switzerland\, 80
 93
ORGANIZER:ales.hrabec@psi.ch
SEQUENCE:10
SUMMARY:IEEE Distinguished Lecture
URL;VALUE=URI:https://events.vtools.ieee.org/m/444430
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-U
 S&quot; style=&quot;font-size: 18.0pt\; mso-ansi-language: EN-US\; mso-fareast-langu
 age: DE-CH\;&quot;&gt;Brain-Inspired Computing Using Magnetic Domain Wall Devices&lt;
 /span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-US&quot; style
 =&quot;font-size: 18.0pt\; mso-ansi-language: EN-US\; mso-fareast-language: DE-
 CH\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-righ
 t: 50.4pt\;&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 18.0pt\; mso-ans
 i-language: EN-US\; mso-fareast-language: DE-CH\;&quot;&gt;Prof. S.N. (Prem) Piram
 anayagam&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-right: 50
 .4pt\;&quot;&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 18.0pt\; mso-ansi-language: E
 N-US\;&quot;&gt;Nanyang Technological University&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;
 &lt;span lang=&quot;EN-US&quot; style=&quot;mso-ansi-language: EN-US\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n
 &lt;p class=&quot;MsoNormal&quot; style=&quot;margin-right: 50.4pt\;&quot;&gt;&lt;span style=&quot;font-size
 : 14.0pt\;&quot;&gt;Neuromorphic computing or brain-inspired computing is consider
 ed 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 ess
 ential to design and fabricate electronic analogues of neurons and synapse
 s. An electronic analogue of a synaptic device should provide multiple res
 istance states. A neuron device should receive multiple inputs and should 
 provide a pulse output when the summation of the multiple inputs exceeds a
  threshold. &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-right: 50.4pt\
 ;&quot;&gt;&lt;span style=&quot;font-size: 14.0pt\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNorm
 al&quot; style=&quot;margin-right: 50.4pt\;&quot;&gt;&lt;span style=&quot;font-size: 14.0pt\;&quot;&gt;We ha
 ve been carrying out investigations on the design and development of vario
 us 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 synapti
 c and neuron devices [2]. Spin-orbit torque helps in achieving DW motion a
 t low energies whereas the use of MTJs helps in translating DW position in
 formation into resistance levels (or voltage pulses) [3]. This talk will s
 ummarize various designs of synthetic neurons synaptic elements and materi
 als [4]. The first half of the talk will be at an introductory level\, aim
 ed at first-year graduate students. The second half will provide details o
 f the latest research. &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-rig
 ht: 50.4pt\;&quot;&gt;&lt;span style=&quot;font-size: 14.0pt\;&quot;&gt;&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p cla
 ss=&quot;MsoNormal&quot; style=&quot;margin-right: 50.4pt\;&quot;&gt;&lt;span style=&quot;font-size: 14.0
 pt\;&quot;&gt;[1] K. Roy\, A Jaiswal\, and P Panda\, &amp;ldquo\;Towards Spike-Based M
 achine Intelligence With Neuromorphic Computing\,&amp;rdquo\; Nature 575\, 607
 -617 (2019). &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-right: 50.4pt
 \;&quot;&gt;&lt;span style=&quot;font-size: 14.0pt\;&quot;&gt;[2] W. L. W. Mah\, J. P. Chan\, K. R
 . Ganesh\, V. B. Naik\, S. N. Piramanayagam\, &amp;ldquo\;Leakage Function in 
 Magnetic Domain Wall Based Artificial Neuron Using Stray Field\,&amp;rdquo\; A
 ppl. Phys. Lett. 123\, 092401 (2023). &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; st
 yle=&quot;margin-right: 50.4pt\;&quot;&gt;&lt;span style=&quot;font-size: 14.0pt\;&quot;&gt;[3] D. Kuma
 r\, H. J. Chung\, J. P. Chan\, T. L. Jin\, S. T. Lim\, S. S. P. Parkin\, R
 . Sbiaa\, S. N. Piramanayagam\, &amp;ldquo\;Ultralow Energy Domain Wall Device
  for Spin-Based Neuromorphic Computing\,&amp;rdquo\; ACS Nano 17\, 6261-6274 (
 2023). &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-right: 50.4pt\;&quot;&gt;&lt;s
 pan style=&quot;font-size: 14.0pt\;&quot;&gt;[4] R. Maddu\, D. Kumar\, S. Bhatti\, S. N
 . Piramanayagam\, &amp;ldquo\;Spintronic Heterostructures for Artificial Intel
 ligence: A Materials Perspective\,&amp;rdquo\; Phys. Stat. Sol. RRL 17\, 22004
 93 (2023).&lt;/span&gt;&lt;/p&gt;
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