Photo of Abhronil Sengupta

Abhronil Sengupta

Assistant Professor

Affiliation(s):

  • School of Electrical Engineering and Computer Science
  • Electrical Engineering
  • Materials Research Institute

111K Electrical Engineering West

sengupta@psu.edu

814-867-4776

Personal or Departmental Website

Research Areas:

Data Science and Artificial Intelligence; Electronic Materials and Devices; Integrated Circuits and Systems

Interest Areas:

Neuromorphic Computing across the stack of sensors, devices, circuits, systems and algorithms

 
 

 

Education

  • BE, Electronics and Telecommunication Engineering, Jadavpur University, 2013
  • Ph D, Electrical and Computer Engineering, Purdue University, 2018

Publications

Journal Articles

  • Shubham Jain, Abhronil Sengupta, Kaushik Roy and Anand Raghunathan, 2021, "RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 40, (2), pp. 326-338
  • Kaveri Mahapatra, Sen Lu, Abhronil Sengupta and Nilanjan Ray Chaudhuri, 2021, "Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing", IEEE Transactions on Smart Grid, 12, (3), pp. 2343-2354
  • Mehul Rastogi, Sen Lu, Nafiul Islam and Abhronil Sengupta, 2021, "On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs", Frontiers in Neuroscience, 14, pp. 603796
  • Kezhou Yang, Akul Malhotra, Sen Lu and Abhronil Sengupta, 2020, "All-Spin Bayesian Neural Networks", IEEE Transactions on Electron Devices, 67, (3), pp. 1340 – 1347
  • Aayush Ankit, Timur Ibrayev, Abhronil Sengupta and Kaushik Roy, 2020, "TraNNsformer: Clustered Pruning on Crossbar-based Architectures for Energy Efficient Neural Networks", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39, (10), pp. 2361 - 2374
  • Amogh Agrawal, Indranil Chakraborty, Deboleena Roy, Utkarsh Saxena, Saima Sharmin, Yong Shim, Gopalakrishnan Srinivasan, Chamika Liyanagedera, Abhronil Sengupta and Kaushik Roy, 2020, "Revisiting Stochastic Computing in the Era of Nano-scale Non-volatile Technologies", IEEE Transactions on Very Large Scale Integration Systems, 28, (12), pp. 2481 - 2494
  • Akul Malhotra, Sen Lu, Kezhou Yang and Abhronil Sengupta, 2020, "Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design", IEEE Transactions on Nanotechnology, 19, pp. 328 - 331
  • Sen Lu and Abhronil Sengupta, 2020, "Exploring the Connection Between Binary and Spiking Neural Networks", Frontiers in Neuroscience, 14, pp. 535
  • Kezhou Yang and Abhronil Sengupta, 2020, "Stochastic Magnetoelectric Neuron for Temporal Information Encoding", Applied Physics Letters, 116, (4), pp. 043701
  • Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu and Kaushik Roy, 2019, "Going deeper in spiking neural networks: Vgg and residual architectures", Frontiers in neuroscience, 13
  • Parami Wijesinghe, Aayush Ankit, Abhronil Sengupta and Kaushik Roy, 2018, "An All-Memristor Deep Spiking Neural Computing System: A Step Toward Realizing the Low-Power Stochastic Brain", IEEE Transactions on Emerging Topics in Computational Intelligence, 2, (5), pp. 345--358
  • Indranil Chakraborty, Gobinda Saha, Abhronil Sengupta and Kaushik Roy, 2018, "Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons", Scientific Reports, 8, (1), pp. 12980
  • Yong Shim, Abhronil Sengupta and Kaushik Roy, 2018, "Biased Random Walk Using Stochastic Switching of Nanomagnets: Application to SAT Solver", IEEE Transactions on Electron Devices, 65, (4), pp. 1617--1624
  • Mei-Chin Chen, Abhronil Sengupta and Kaushik Roy, 2018, "Magnetic Skyrmion as a Spintronic Deep Learning Spiking Neuron Processor", IEEE Transactions on Magnetics, 54, (8), pp. 1--7
  • Abhronil Sengupta and Kaushik Roy, 2018, "Neuromorphic computing enabled by physics of electron spins: Prospects and perspectives", Applied Physics Express, 11, (3), pp. 030101
  • Kaushik Roy, Abhronil Sengupta and Yong Shim, 2018, "Perspective: Stochastic magnetic devices for cognitive computing", Journal of Applied Physics, 123, (21), pp. 210901
  • Bing Han, Aayush Ankit, Abhronil Sengupta and Kaushik Roy, 2017, "Cross-Layer Design Exploration for Energy-Quality Tradeoffs in Spiking and Non-Spiking Deep Artificial Neural Networks", IEEE Transactions on Multi-Scale Computing Systems, (1), pp. 1--1
  • Abhronil Sengupta and Kaushik Roy, 2017, "Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing", Applied Physics Reviews, 4, (4), pp. 041105
  • Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan and Kaushik Roy, 2017, "Energy-efficient object detection using semantic decomposition", IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25, (9), pp. 2673--2677
  • Abhronil Sengupta, Chamika Mihiranga Liyanagedera, Byunghoo Jung and Kaushik Roy, 2017, "Magnetic tunnel junction as an on-chip temperature sensor", Scientific reports, 7, (1), pp. 11764
  • Chamika M Liyanagedera, Abhronil Sengupta, Akhilesh Jaiswal and Kaushik Roy, 2017, "Stochastic spiking neural networks enabled by magnetic tunnel junctions: From nontelegraphic to telegraphic switching regimes", Physical Review Applied, 8, (6), pp. 064017
  • Yong Shim, Shuhan Chen, Abhronil Sengupta and Kaushik Roy, 2017, "Stochastic spin-orbit torque devices as elements for bayesian inference", Scientific Reports, 7, (1), pp. 14101
  • Priyadarshini Panda, Abhronil Sengupta and Kaushik Roy, 2017, "Energy-efficient and improved image recognition with conditional deep learning", ACM Journal on Emerging Technologies in Computing Systems (JETC), 13, (3), pp. 33
  • Abhronil Sengupta and Kaushik Roy, 2016, "A vision for all-spin neural networks: A device to system perspective", IEEE Transactions on Circuits and Systems I: Regular Papers, 63, (12), pp. 2267--2277
  • Deliang Fan, Mrigank Sharad, Abhronil Sengupta and Kaushik Roy, 2016, "Hierarchical temporal memory based on spin-neurons and resistive memory for energy-efficient brain-inspired computing", IEEE transactions on neural networks and learning systems, 27, (9), pp. 1907--1919
  • Abhronil Sengupta, Aparajita Banerjee and Kaushik Roy, 2016, "Hybrid spintronic-CMOS spiking neural network with on-chip learning: Devices, circuits, and systems", Physical Review Applied, 6, (6), pp. 064003
  • Gopalakrishnan Srinivasan, Abhronil Sengupta and Kaushik Roy, 2016, "Magnetic tunnel junction based long-term short-term stochastic synapse for a spiking neural network with on-chip STDP learning", Scientific Reports, 6, pp. 29545
  • Abhronil Sengupta, Priyadarshini Panda, Parami Wijesinghe, Yusung Kim and Kaushik Roy, 2016, "Magnetic tunnel junction mimics stochastic cortical spiking neurons", Scientific Reports, 6, pp. 30039
  • Abhronil Sengupta, Maryam Parsa, Bing Han and Kaushik Roy, 2016, "Probabilistic deep spiking neural systems enabled by magnetic tunnel junction", IEEE Transactions on Electron Devices, 63, (7), pp. 2963--2970
  • Abhronil Sengupta, Yong Shim and Kaushik Roy, 2016, "Proposal for an all-spin artificial neural network: Emulating neural and synaptic functionalities through domain wall motion in ferromagnets", IEEE transactions on biomedical circuits and systems, 10, (6), pp. 1152--1160
  • Abhronil Sengupta and Kaushik Roy, 2016, "Short-term plasticity and long-term potentiation in magnetic tunnel junctions: Towards volatile synapses", Physical Review Applied, 5, (2), pp. 024012
  • Zubair Al Azim, Abhronil Sengupta, Syed Shakib Sarwar and Kaushik Roy, 2016, "Spin-torque sensors for energy efficient high-speed long interconnects", IEEE Transactions on Electron Devices, 63, (2), pp. 800--808
  • Xuanyao Fong, Yusung Kim, Karthik Yogendra, Deliang Fan, Abhronil Sengupta, Anand Raghunathan and Kaushik Roy, 2016, "Spin-transfer torque devices for logic and memory: Prospects and perspectives", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 35, (1), pp. 1--22
  • Abhronil Sengupta, Zubair Al Azim, Xuanyao Fong and Kaushik Roy, 2015, "Spin-orbit torque induced spike-timing dependent plasticity", Applied Physics Letters, 106, (9), pp. 093704
  • Abhronil Sengupta, Sri Harsha Choday, Yusung Kim and Kaushik Roy, 2015, "Spin orbit torque based electronic neuron", Applied Physics Letters, 106, (14), pp. 143701
  • Saugat Bhattacharyya, Abhronil Sengupta, Tathagatha Chakraborti, Amit Konar and DN Tibarewala, 2014, "Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata", Medical & biological engineering & computing, 52, (2), pp. 131--139

Conference Proceedings

  • Sonali Singh, Anup Sharma, Sen Lu, Abhronil Sengupta, Vijaykrishnan Narayanan and Chita Das, 2021, "Gesture-SNN: Co-optimizing accuracy, latency and energy of SNNs for neuromorphic vision sensors", pp. 1-6
  • Sonali Singh, Anup Sharma, Nicholas Jao, Ashutosh Pattnaik, Sen Lu, Kezhou Yang, Abhronil Sengupta, Vijaykrishnan Narayanan and Chita Das, 2020, "NEBULA: A Neuromorphic Spin-Based Ultra-Low Power Architecture for SNNs and ANNs", pp. 363-376
  • Gopalakrishnan Srinivasan, Chankyu Lee, Abhronil Sengupta, Priyadarshini Panda, Syed Shakib Sarwar and Kaushik Roy, 2020, "Training Deep Spiking Neural Networks for Energy Efficient Neuromorphic Computing", pp. 8549-8553
  • Nicholas Jao, Akshay Krishna Ramanathan, Abhronil Sengupta, John Sampson and Vijaykrishnan Narayanan, 2019, "Programmable Non-Volatile Memory Design Featuring Reconfigurable In-Memory Operations", pp. 1--5
  • Aayush Ankit, Abhronil Sengupta and Kaushik Roy, 2018, "Neuromorphic Computing Across the Stack: Devices, Circuits and Architectures", pp. 1--6
  • Abhronil Sengupta, Gopalakrishnan Srinivasan, Deboleena Roy and Kaushik Roy, 2018, "Stochastic Inference and Learning Enabled by Magnetic Tunnel Junctions", pp. 15--6
  • Gopalakrishnan Srinivasan, Abhronil Sengupta and Kaushik Roy, 2017, "Magnetic tunnel junction enabled all-spin stochastic spiking neural network", pp. 530--535
  • Abhronil Sengupta, Aayush Ankit and Kaushik Roy, 2017, "Performance analysis and benchmarking of all-spin spiking neural networks", pp. 4557--4563
  • Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda and Kaushik Roy, 2017, "RESPARC: A reconfigurable and energy-efficient architecture with memristive crossbars for deep spiking neural networks", pp. 27
  • Aayush Ankit, Abhronil Sengupta and Kaushik Roy, 2017, "TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design", pp. 533--540
  • Priyadarshini Panda, Abhronil Sengupta and Kaushik Roy, 2016, "Conditional deep learning for energy-efficient and enhanced pattern recognition", pp. 475--480
  • Priyadarshini Panda, Abhronil Sengupta, Syed Shakib Sarwar, Gopalakrishnan Srinivasan, Swagath Venkataramani, Anand Raghunathan and Kaushik Roy, 2016, "Cross-layer approximations for neuromorphic computing: from devices to circuits and systems", pp. 98
  • Yong Shim, Abhronil Sengupta and Kaushik Roy, 2016, "Low-power approximate convolution computing unit with domain-wall motion based spin-memristor for image processing applications", pp. 21
  • Abhronil Sengupta, Priyadarshini Panda, Anand Raghunathan and Kaushik Roy, 2016, "Neuromorphic computing enabled by spin-transfer torque devices", pp. 32--37
  • Bing Han, Abhronil Sengupta and Kaushik Roy, 2016, "On the energy benefits of spiking deep neural networks: A case study", pp. 971--976
  • Abhronil Sengupta, Karthik Yogendra, Deliang Fan and Kaushik Roy, 2016, "Prospects of efficient neural computing with arrays of magneto-metallic neurons and synapses", pp. 115--120
  • Abhronil Sengupta, Karthik Yogendra and Kaushik Roy, 2016, "Spintronic devices for ultra-low power neuromorphic computation (Special session paper)", pp. 922--925
  • Abhronil Sengupta, Bing Han and Kaushik Roy, 2016, "Toward a spintronic deep learning spiking neural processor", pp. 544--547
  • Abhronil Sengupta, Akhilesh Jaiswal and Kaushik Roy, 2016, "True random number generation using voltage controlled spin-dice", pp. 1--2
  • Abhronil Sengupta and Kaushik Roy, 2015, "Spin-transfer torque magnetic neuron for low power neuromorphic computing", pp. 1--7
  • Tathagata Chakraborti, Abhronil Sengupta, Abhishek Midya, Amit Konar and Somnath Sengupta, 2013, "3-D model assisted facial error concealment technique using regenerative particle filter based tracking", pp. 1--6

Abstracts

  • Abhronil Sengupta, Yong Shim and Kaushik Roy, 2018, "Stochastic Switching of Nanomagnets for Post-CMOS Computing", Bulletin of the American Physical Society

Other

  • Abhronil Sengupta, 2018, "Efficient Neuromorphic Computing Enabled by Spin-Transfer Torque: Devices, Circuits and Systems"

Research Projects

Honors and Awards

  • IEEE Circuits and Systems (CAS) Society Outstanding Young Author Award, IEEE, 2019
  • Best Paper Award at the 2018 IEEE Workshop on Signal Processing Systems (SiPS), 2018
  • Schmidt Science Fellows Award nominee, Purdue University, 2017
  • Bilsland Dissertation Fellowship, Purdue University, 2017
  • Michael and Katherine Birck Fellowship, Purdue University, 2013 - 2015
  • T.P. Saha Memorial Gold Medal, D.Mukhopadhyay Memorial and S.Deb Memorial Gold Medals, Jadavpur University, 2013
  • WISE Fellowship, DAAD (German Academic Exchange Service), 2012

Service

Service to Penn State:

Service to External Organizations:

 


 

About

The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

School of Electrical Engineering and Computer Science

The Pennsylvania State University

207 Electrical Engineering West

University Park, PA 16802

814-863-6740

Department of Computer Science and Engineering

814-865-9505

Department of Electrical Engineering

814-865-7667