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UID:www.tcs.tifr.res.in/event/1051
DTSTAMP:20230914T125948Z
SUMMARY:Artificial Intelligence using Emerging Eevices and Architectures
DESCRIPTION:Speaker: Dr. Debanjan Bhowmik (Assistant Professor\nDepartment 
 of Electrical Engineering\nIndian Institute of Technology Delhi)\n\nAbstra
 ct: \nAbstract: Artificial Intelligence (AI)/ Machine Learning (ML)/ Neur
 al Network (NN) algorithms are being widely used currently for various app
 lications that include self driving cars\, virtual assistants on smartphon
 es and other devices\, etc. However\, the memory-computing separation in e
 xisting computer hardware makes implementation of these algorithms on the 
 hardware inefficient in terms of power and speed. As a result\, new device
 s and architectures are being proposed to run these algorithms more effici
 ently.\nIn this context\, I will discussed the latest work carried out in 
 our research group on the implementation of AI/ML/NN algorithms in a cross
 bar based in-memory computing architecture\, as well as a quantum architec
 ture. For the former\, we have used both spin based devices (spin orbit to
 rque driven domain wall devices) [1\,2\,3] and charge based devices (a sin
 gle conventional silicon transistor synapse) [4]. We have used both non-sp
 iking NN algorithms\, used abundantly in the ML community\, [2\,4] as well
  as spiking NN algorithms\, inspired from the working of the brain [3]. Fo
 r the latter\, we have proposed a novel quantum algorithm\, implemented it
  on the "qiskit" simulation framework and shown very high classification a
 ccuracy on different popular ML datasets [5].\nReferences:\n1. Debanjan Bh
 owmik et al. "Deterministic domain wall motion orthogonal to current flow 
 due to spin orbit torque". Scientific Reports \, Vol. 5\, 11823 (2015)\n2.
  Debanjan Bhowmik et al. "On-chip learning for domain wall synapse based F
 ully Connected Neural Network". Journal of Magnetism and Magnetic Material
 s Vol. 489\, 165434 (2019)\n3. Upasana Sahu\, Aadit Pandey\, Kushaagra Goy
 al and Debanjan Bhowmik. "Spike time dependent plasticity (STDP) enabled l
 earning in spiking neural networks using domain wall based synapses and ne
 urons". AIP Advances Vol. 9\, 12 (2019)\n4. Nilabjo Dey\, Janak Sharda\, U
 tkarsh Saxena\, Divya Kaushik\, Utkarsh Singh\, Debanjan Bhowmik. "On-Chip
  Learning in a Conventional Silicon MOSFET Based Analog Hardware Neural Ne
 twork". IEEE Biomedical Circuits and Systems Conference (BioCAS)\, Nara\, 
 Japan (2019)\n5. S. Adhikary\, S. Dangwal and D. Bhowmik\, Supervised lear
 ning with a quantum classifier using multi-level systems\, Quantum Informa
 tion Processing 19\, 89 (2020).\nBio: Dr. Debanjan Bhowmik is currently a
 n Assistant Professor in the Department of Electrical Engineering\, Indian
  Institute of Technology Delhi. He obtained his BTech degree in Electrical
  Engineering from Indian Institute of Technology Kharagpur in 2010. He obt
 ained his PhD degree from University of California Berkeley in 2015\, work
 ing in the field of nano magnetism and spintronics. Currently at IIT Delhi
  he works on Artificial Intelligence using emerging devices like spintroni
 c devices and emerging architectures like in-memory computing architecture
  and quantum architecture.\n
URL:https://www.tcs.tifr.res.in/web/events/1051
DTSTART;TZID=Asia/Kolkata:20200211T140000
DTEND;TZID=Asia/Kolkata:20200211T150000
LOCATION:A-201 (STCS Seminar Room)
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