Souvik Kundu

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 70

Top Topics

Fpga Implementation

Object Detection

Resource Constraints

Top Venues

Publications

Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation. CoRR (2024)
Souvik Kundu, Rui-Jie Zhu, Akhilesh Jaiswal, Peter A. Beerel
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural Networks: from Algorithms to Technology. ICASSP (2024)
Sreetama Sarkar, Gourav Datta, Souvik Kundu, Kai Zheng, Chirayata Bhattacharyya, Peter A. Beerel
MaskVD: Region Masking for Efficient Video Object Detection. CoRR (2024)
Souvik Kundu, Sharath Nittur Sridhar, Maciej Szankin, Sairam Sundaresan
Sensi-Bert: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient Language Model. ICASSP (2024)
Zeyu Liu, Souvik Kundu, Anni Li, Junrui Wan, Lianghao Jiang, Peter Anthony Beerel
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models. CoRR (2024)
Sreetama Sarkar, Souvik Kundu, Peter A. Beerel
Linearizing Models for Efficient yet Robust Private Inference. CoRR (2024)
Souvik Kundu, Anthony Sarah, Vinay Joshi, Om Ji Omer, Sreenivas Subramoney
Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware. CoRR (2024)
Sreetama Sarkar, Souvik Kundu, Kai Zheng, Peter A. Beerel
Block Selective Reprogramming for On-device Training of Vision Transformers. CoRR (2024)
Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Tiffany Liu, Peter A. Beerel
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference. ICLR (2023)
Shashank Nag, Gourav Datta, Souvik Kundu, Nitin Chandrachoodan, Peter A. Beerel
ViTA: A Vision Transformer Inference Accelerator for Edge Applications. CoRR (2023)
Shashank Nag, Gourav Datta, Souvik Kundu, Nitin Chandrachoodan, Peter A. Beerel
ViTA: A Vision Transformer Inference Accelerator for Edge Applications. ISCAS (2023)
Dake Chen, Yuke Zhang, Souvik Kundu, Chenghao Li, Peter A. Beerel
RNA-ViT: Reduced-Dimension Approximate Normalized Attention Vision Transformers for Latency Efficient Private Inference. ICCAD (2023)
Souvik Kundu, Yuke Zhang, Dake Chen, Peter A. Beerel
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference. CoRR (2023)
Yuke Zhang, Dake Chen, Souvik Kundu, Haomei Liu, Ruiheng Peng, Peter A. Beerel
C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference. CoRR (2023)
Sharath Nittur Sridhar, Souvik Kundu, Sairam Sundaresan, Maciej Szankin, Anthony Sarah
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning. CoRR (2023)
Gourav Datta, Zeyu Liu, Md. Abdullah-Al Kaiser, Souvik Kundu, Joe Mathai, Zihan Yin, Ajey P. Jacob, Akhilesh R. Jaiswal, Peter A. Beerel
In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision. ICASSP (2023)
Yue Hu, Xinan Ye, Yifei Liu, Souvik Kundu, Gourav Datta, Srikar Mutnuri, Namo Asavisanu, Nora Ayanian, Konstantinos Psounis, Peter A. Beerel
FireFly A Synthetic Dataset for Ember Detection in Wildfire. CoRR (2023)
Souvik Kundu, Yuke Zhang, Dake Chen, Peter A. Beerel
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference. CVPR Workshops (2023)
Souvik Kundu, Rui-Jie Zhu, Akhilesh Jaiswal, Peter A. Beerel
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology. CoRR (2023)
Md. Abdullah-Al Kaiser, Gourav Datta, Sreetama Sarkar, Souvik Kundu, Zihan Yin, Manas Garg, Ajey P. Jacob, Peter A. Beerel, Akhilesh R. Jaiswal
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M). CoRR (2023)
Souvik Kundu, Sharath Nittur Sridhar, Maciej Szankin, Sairam Sundaresan
Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT. CoRR (2023)
Souvik Kundu, Sairam Sundaresan, Sharath Nittur Sridhar, Shunlin Lu, Han Tang, Peter A. Beerel
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs. CoRR (2023)
Yuke Zhang, Dake Chen, Souvik Kundu, Haomei Liu, Ruiheng Peng, Peter A. Beerel
PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference. DAC (2023)
Fang Chen, Gourav Datta, Souvik Kundu, Peter A. Beerel
Self-Attentive Pooling for Efficient Deep Learning. WACV (2023)
Yuke Zhang, Dake Chen, Souvik Kundu, Chenghao Li, Peter A. Beerel
SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation. ICCV (2023)
Sharath Nittur Sridhar, Souvik Kundu, Sairam Sundaresan, Maciej Szankin, Anthony Sarah
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning. ICCV (Workshops) (2023)
Souvik Kundu, Sairam Sundaresan, Massoud Pedram, Peter A. Beerel
FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-off between Accuracy and Robustness. WACV (2023)
Souvik Kundu, Sairam Sundaresan, Sharath Nittur Sridhar, Shunlin Lu, Han Tang, Peter A. Beerel
Sparse Mixture Once-for-all Adversarial Training for Efficient in-situ Trade-off between Accuracy and Robustness of DNNs. ICASSP (2023)
Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, Salman Avestimehr
Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? Trans. Mach. Learn. Res. 2023 (2023)
Yue Hu, Xinan Ye, Yifei Liu, Souvik Kundu, Gourav Datta, Srikar Mutnuri, Namo Asavisanu, Nora Ayanian, Konstantinos Psounis, Peter A. Beerel
FireFly: A Synthetic Dataset for Ember Detection in Wildfire. ICCV (Workshops) (2023)
Haonan Wang, Connor Imes, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters
Quantpipe: Applying Adaptive Post-Training Quantization For Distributed Transformer Pipelines In Dynamic Edge Environments. ICASSP (2023)
Souvik Kundu, Shunlin Lu, Yuke Zhang, Jacqueline Liu, Peter A. Beerel
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference. CoRR (2023)
Md. Abdullah-Al Kaiser, Gourav Datta, Sreetama Sarkar, Souvik Kundu, Zihan Yin, Manas Garg, Ajey P. Jacob, Peter A. Beerel, Akhilesh R. Jaiswal
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M). ACM Great Lakes Symposium on VLSI (2023)
Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee, Salman Avestimehr
Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients. Trans. Mach. Learn. Res. 2023 (2023)
Souvik Kundu, Yao Fu, Bill Ye, Peter A. Beerel, Massoud Pedram
Toward Adversary-aware Non-iterative Model Pruning through Dynamic Network Rewiring of DNNs. ACM Trans. Embed. Comput. Syst. 21 (5) (2022)
Haonan Wang, Connor Imes, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters
QuantPipe: Applying Adaptive Post-Training Quantization for Distributed Transformer Pipelines in Dynamic Edge Environments. CoRR (2022)
Souvik Kundu, Shikai Wang, Qirui Sun, Peter A. Beerel, Massoud Pedram
BMPQ: Bit-Gradient Sensitivity-Driven Mixed-Precision Quantization of DNNs from Scratch. DATE (2022)
Gourav Datta, Souvik Kundu, Zihan Yin, Joe Mathai, Zeyu Liu, Zixu Wang, Mulin Tian, Shunlin Lu, Ravi Teja Lakkireddy, Andrew Schmidt, Wael Abd-Almageed, Ajey Jacob, Akhilesh R. Jaiswal, Peter A. Beerel
M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking. VLSI-SoC (2022)
Gourav Datta, Zeyu Liu, Md. Abdullah-Al Kaiser, Souvik Kundu, Joe Mathai, Zihan Yin, Ajey P. Jacob, Akhilesh R. Jaiswal, Peter A. Beerel
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision. CoRR (2022)
Yang Hu, Connor Imes, Xuanang Zhao, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters
PipeEdge: Pipeline Parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices. DSD (2022)
Fang Chen, Gourav Datta, Souvik Kundu, Peter A. Beerel
Self-Attentive Pooling for Efficient Deep Learning. CoRR (2022)
Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, Salman Avestimehr
Federated Sparse Training: Lottery Aware Model Compression for Resource Constrained Edge. CoRR (2022)
Yue Niu, Saurav Prakash, Souvik Kundu, Sunwoo Lee, Salman Avestimehr
Federated Learning of Large Models at the Edge via Principal Sub-Model Training. CoRR (2022)
Gourav Datta, Souvik Kundu, Zihan Yin, Ravi Teja Lakkireddy, Joe Mathai, Ajey Jacob, Peter A. Beerel, Akhilesh R. Jaiswal
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications. CoRR (2022)
Souvik Kundu, Sairam Sundaresan, Massoud Pedram, Peter A. Beerel
A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness. CoRR (2022)
Souvik Kundu, Gourav Datta, Massoud Pedram, Peter A. Beerel
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression. CoRR (2021)
Souvik Kundu, Sairam Sundaresan
AttentionLite: Towards Efficient Self-Attention Models for Vision. ICASSP (2021)
Gourav Datta, Souvik Kundu, Peter A. Beerel
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding. IJCNN (2021)
Souvik Kundu, Shikai Wang, Qirui Sun, Peter A. Beerel, Massoud Pedram
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch. CoRR (2021)
Souvik Kundu, Mahdi Nazemi, Peter A. Beerel, Massoud Pedram
DNR: A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs. ASP-DAC (2021)
Souvik Kundu, Qirui Sun, Yao Fu, Massoud Pedram, Peter A. Beerel
Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation. NeurIPS (2021)
Souvik Kundu, Sairam Sundaresan
AttentionLite: Towards Efficient Self-Attention Models for Vision. CoRR (2021)
Gourav Datta, Souvik Kundu, Akhilesh R. Jaiswal, Peter A. Beerel
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification. CoRR (2021)
Yang Hu, Connor Imes, Xuanang Zhao, Souvik Kundu, Peter A. Beerel, Stephen P. Crago, John Paul Walters
Pipeline Parallelism for Inference on Heterogeneous Edge Computing. CoRR (2021)
Gourav Datta, Souvik Kundu, Peter A. Beerel
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding. CoRR (2021)
Souvik Kundu, Massoud Pedram, Peter A. Beerel
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise. ICCV (2021)
Souvik Kundu, Massoud Pedram, Peter A. Beerel
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise. CoRR (2021)
Souvik Kundu, Gourav Datta, Massoud Pedram, Peter A. Beerel
Spike-Thrift: Towards Energy-Efficient Deep Spiking Neural Networks by Limiting Spiking Activity via Attention-Guided Compression. WACV (2021)
Souvik Kundu, Gourav Datta, Peter A. Beerel, Massoud Pedram
qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit. CoRR (2020)
Souvik Kundu, Mahdi Nazemi, Massoud Pedram, Keith M. Chugg, Peter A. Beerel
Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks. IEEE Trans. Computers 69 (7) (2020)
Souvik Kundu, Mahdi Nazemi, Massoud Pedram, Keith M. Chugg, Peter A. Beerel
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks. CoRR (2020)
Souvik Kundu, Hesham Mostafa, Sharath Nittur Sridhar, Sairam Sundaresan
Attention-based Image Upsampling. CoRR (2020)
Souvik Kundu, Mahdi Nazemi, Peter A. Beerel, Massoud Pedram
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs. CoRR (2020)
Arash Fayyazi, Souvik Kundu, Shahin Nazarian, Peter A. Beerel, Massoud Pedram
CSrram: Area-Efficient Low-Power Ex-Situ Training Framework for Memristive Neuromorphic Circuits Based on Clustered Sparsity. ISVLSI (2019)
Gourav Datta, Haolin Cong, Souvik Kundu, Peter A. Beerel
Metastability-Resilient Synchronization FIFO for SFQ Logic. CoRR (2019)
Souvik Kundu, Saurav Prakash, Haleh Akrami, Peter A. Beerel, Keith M. Chugg
A Pre-defined Sparse Kernel Based Convolution for Deep CNNs. CoRR (2019)
Souvik Kundu, Saurav Prakash, Haleh Akrami, Peter A. Beerel, Keith M. Chugg
pSConv: A Pre-defined S parse Kernel Based Convolution for Deep CNNs. Allerton (2019)
Arash Fayyazi, Souvik Kundu, Shahin Nazarian, Peter A. Beerel, Massoud Pedram
SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application. CoRR (2018)
Sourya Dey, Diandian Chen, Zongyang Li, Souvik Kundu, Kuan-Wen Huang, Keith M. Chugg, Peter A. Beerel
A Highly Parallel FPGA Implementation of Sparse Neural Network Training. ReConFig (2018)
Sourya Dey, Diandian Chen, Zongyang Li, Souvik Kundu, Kuan-Wen Huang, Keith M. Chugg, Peter A. Beerel
A Highly Parallel FPGA Implementation of Sparse Neural Network Training. CoRR (2018)