Souvik Kundu

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Peter BeerelProfessor of Electrical and Computer Engineering, USCAdresse e-mail validée de usc.edu
Massoud PedramCharles Lee Powell Professor of Electrical Engineering & Computer Science, Univ of SouthernAdresse e-mail validée de usc.edu
Gourav DattaAmazon Inc., University of Southern CaliforniaAdresse e-mail validée de usc.edu
Salman AvestimehrDean's Professor and Director of USC-Amazon Center on Trustworthy AI, USCAdresse e-mail validée de usc.edu
Zhangyang (Atlas) WangUniversity of Texas at Austin & Picsart AI ResearchAdresse e-mail validée de utexas.edu
Hesham MostafaResearch scientist, Intel CorporationAdresse e-mail validée de intel.com
Yingyan (Celine) LinAssociate Professor, Georgia Institute of TechnologyAdresse e-mail validée de gatech.edu
Eric XingPresident at Mohamed bin Zayed University of AI, Professor of Computer Science, Carnegie Mellon UAdresse e-mail validée de cs.cmu.edu
Hongyi WangSenior Project Scientist, Carnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
Mikhail YurochkinResearch Staff Member, IBM Research and MIT-IBM Watson AI LabAdresse e-mail validée de umich.edu
Yuekai SunUniversity of MichiganAdresse e-mail validée de umich.edu
Tushar KrishnaAssociate Professor, Georgia TechAdresse e-mail validée de ece.gatech.edu
Tuo ZhaoAssistant Professor, Georgia TechAdresse e-mail validée de gatech.edu
Sreenivas SubramoneyIntel CorporationAdresse e-mail validée de intel.com
Priyadarshini (Priya) PandaAssistant Professor, Electrical Engineering, Yale UniversityAdresse e-mail validée de yale.edu

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Souvik Kundu

Research Scientist, Intel Labs; Ph.D - University of Southern California

Adresse e-mail validée de intel.com - Page d'accueil

Efficient AI Foundation models Machine learning Trustworthy Machine Learning Low power design


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Spike-thrift: Towards energy-efficient deep spiking neural networks by limiting spiking activity via attention-guided compression S Kundu, G Datta, M Pedram, PA Beerel IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2021 …, 2021	104	2021
DNR: A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs S Kundu, M Nazemi, PA Beerel, M Pedram Proceedings of the 26th ASP-DAC 2021, 344-350, 2021	60	2021
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training With Crafted Input Noise S Kundu, M Pedram, PA Beerel IEEE/CVF International Conference on Computer Vision (ICCV 2021), 5209-5218, 2021	57	2021
Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks S Kundu, M Nazemi, M Pedram, KM Chugg, PA Beerel IEEE Transactions on Computers 2020 69 (7), 1045-1058, 2020	50	2020
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding G Datta, S Kundu, PA Beerel IJCNN 2021, 2021	37	2021
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications S Kundu, G Datta, Z Yin, RT Lakkireddy, PA Beerel, A Jacob, ARE Jaiswal Nature Scientific Reports 2022, 2022	30*	2022
ACE-SNN: Algorithm-Hardware Co-Design of Energy-efficient & Low-Latency Deep Spiking Neural Networks for 3D Image Recognition G Datta, S Kundu, A Jaiswal, PA Beerel Frontiers in Neuroscience, 2022	29*	2022
AttentionLite: Towards Efficient Self-Attention Models for Vision S Kundu, S Sundaresan ICASSP 2021, 2021	27	2021
Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation S Kundu, Q Sun, Y Fu, M Pedram, P Beerel Advances in Neural Information Processing Systems (NeurIPS 2021) 34, 2021	23	2021
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference S Kundu, S Lu, Y Zhang, J Liu, PA Beerel International Conference on Learning Representation (ICLR) 2023., 2023	22	2023
A highly parallel FPGA implementation of sparse neural network training S Dey, D Chen, Z Li, S Kundu, KW Huang, KM Chugg, PA Beerel 2018 International Conference on ReConFigurable Computing and FPGAs …, 2018	20	2018
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression S Kundu, G Datta, M Pedram, PA Beerel 2nd Sparse Neural Networks Workshop (co-located with ICML 2022), 2021	19	2021
pSConv: A Pre-defined S parse Kernel Based Convolution for Deep CNNs S Kundu, S Prakash, H Akrami, PA Beerel, KM Chugg 2019 57th Annual Allerton Conference on Communication, Control, and …, 2019	19*	2019
Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? S Kundu, S Babakniya, S Prakash, Y Niu, S Avestimehr Transactions on Machine Learning Research (TMLR), 2023	17*	2023
Pipeedge: Pipeline parallelism for large-scale model inference on heterogeneous edge devices Y Hu, C Imes, X Zhao, S Kundu, PA Beerel, SP Crago, JP Walters 2022 25th Euromicro Conference on Digital System Design (DSD), 298-307, 2022	15*	2022
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch S Kundu, S Wang, Q Sun, PA Beerel, M Pedram Design Automation and Test in Europe (DATE) 2022, 2021	14	2021
Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients Y Niu, S Prakash, S Kundu, S Lee, S Avestimehr Transactions in Machine Learning Research (TMLR), 2023	13*	2023
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking S Kundu, G Datta, Z Yin, J Mathai, Z Liu, Z Wang, M Tian, S Lu, ... VLSI-SoC 2022, 2022	13*	2022
Understanding of Emotion Perception from Art D Bose, K Somandepalli, S Kundu, R Lahiri, J Gratch, S Narayanan ICCV CLVL Workshop 2021, 2021	12	2021
Attention-based Image Upsampling S Kundu, H Mostafa, SN Sridhar, S Sundaresan arXiv preprint, 2020	12	2020

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