A Highly Parallel FPGA Implementation of Sparse Neural Network Training. - Sourya Dey; Diandian Chen; Zongyang Li; Souvik Kundu 002; Kuan-Wen Huang; Keith M. Chugg; Peter A. Beerel (ReConFig, 2018)

SpRRAM: A Predefined Sparsity Based Memristive Neuromorphic Circuit for Low Power Application. - Arash Fayyazi; Souvik Kundu 002; Shahin Nazarian; Peter A. Beerel; Massoud Pedram (CoRR, 2018)

pSConv: A Pre-defined S parse Kernel Based Convolution for Deep CNNs. - Souvik Kundu 002; Saurav Prakash; Haleh Akrami; Peter A. Beerel; Keith M. Chugg (Allerton, 2019)

CSrram: Area-Efficient Low-Power Ex-Situ Training Framework for Memristive Neuromorphic Circuits Based on Clustered Sparsity. - Arash Fayyazi; Souvik Kundu 002; Shahin Nazarian; Peter A. Beerel; Massoud Pedram (ISVLSI, 2019)

A Pre-defined Sparse Kernel Based Convolution for Deep CNNs. - Souvik Kundu 002; Saurav Prakash; Haleh Akrami; Peter A. Beerel; Keith M. Chugg (CoRR, 2019)

Metastability-Resilient Synchronization FIFO for SFQ Logic. - Gourav Datta; Haolin Cong; Souvik Kundu 002; Peter A. Beerel (CoRR, 2019)

qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit. - Souvik Kundu 002; Gourav Datta; Peter A. Beerel; Massoud Pedram (CoRR, 2020)

A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs. - Souvik Kundu 002; Mahdi Nazemi; Peter A. Beerel; Massoud Pedram (CoRR, 2020)

Attention-based Image Upsampling. - Souvik Kundu 002; Hesham Mostafa; Sharath Nittur Sridhar; Sairam Sundaresan (CoRR, 2020)

Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks. - Souvik Kundu 002; Mahdi Nazemi; Massoud Pedram; Keith M. Chugg; Peter A. Beerel (IEEE Trans. Computers, 2020)

DNR: A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs. - Souvik Kundu 002; Mahdi Nazemi; Peter A. Beerel; Massoud Pedram (ASP-DAC, 2021)

AttentionLite: Towards Efficient Self-Attention Models for Vision. - Souvik Kundu 002; Sairam Sundaresan (ICASSP, 2021)

HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise. - Souvik Kundu 002; Massoud Pedram; Peter A. Beerel (ICCV, 2021)

Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding. - Gourav Datta; Souvik Kundu 002; Peter A. Beerel (IJCNN, 2021)

Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation. - Souvik Kundu 002; Qirui Sun; Yao Fu; Massoud Pedram; Peter A. Beerel (NeurIPS, 2021)

Spike-Thrift: Towards Energy-Efficient Deep Spiking Neural Networks by Limiting Spiking Activity via Attention-Guided Compression. - Souvik Kundu 002; Gourav Datta; Massoud Pedram; Peter A. Beerel (WACV, 2021)

HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification. - Gourav Datta; Souvik Kundu 002; Akhilesh R. Jaiswal; Peter A. Beerel (CoRR, 2021)

Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression. - Souvik Kundu 002; Gourav Datta; Massoud Pedram; Peter A. Beerel (CoRR, 2021)

Pipeline Parallelism for Inference on Heterogeneous Edge Computing. - Yang Hu; Connor Imes; Xuanang Zhao; Souvik Kundu 002; Peter A. Beerel; Stephen P. Crago; John Paul Walters (CoRR, 2021)

BMPQ: Bit-Gradient Sensitivity-Driven Mixed-Precision Quantization of DNNs from Scratch. - Souvik Kundu 002; Shikai Wang; Qirui Sun; Peter A. Beerel; Massoud Pedram (DATE, 2022)

PipeEdge: Pipeline Parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices. - Yang Hu; Connor Imes; Xuanang Zhao; Souvik Kundu 002; Peter A. Beerel; Stephen P. Crago; John Paul Walters (DSD, 2022)

P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking. - Gourav Datta; Souvik Kundu 002; Zihan Yin; Joe Mathai; Zeyu Liu 003; Zixu Wang; Mulin Tian; Shunlin Lu; Ravi Teja Lakkireddy; Andrew G. Schmidt; Wael Abd-Almageed; Ajey P. Jacob; et al. (VLSI-SoC, 2022)

P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications. - Gourav Datta; Souvik Kundu 002; Zihan Yin; Ravi Teja Lakkireddy; Joe Mathai; Ajey P. Jacob; Peter A. Beerel; Akhilesh R. Jaiswal (CoRR, 2022)

A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness. - Souvik Kundu 002; Sairam Sundaresan; Massoud Pedram; Peter A. Beerel (CoRR, 2022)

Federated Sparse Training: Lottery Aware Model Compression for Resource Constrained Edge. - Sara Babakniya; Souvik Kundu 002; Saurav Prakash; Yue Niu; Salman Avestimehr (CoRR, 2022)

Federated Learning of Large Models at the Edge via Principal Sub-Model Training. - Yue Niu; Saurav Prakash; Souvik Kundu 002; Sunwoo Lee 001; Salman Avestimehr (CoRR, 2022)

Toward Adversary-aware Non-iterative Model Pruning through Dynamic Network Rewiring of DNNs. - Souvik Kundu 002; Yao Fu; Bill Ye; Peter A. Beerel; Massoud Pedram (ACM Trans. Embed. Comput. Syst., 2022)

Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference. - Souvik Kundu 002; Yuke Zhang; Dake Chen; Peter A. Beerel (CVPR Workshops, 2023)

C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference. - Yuke Zhang; Dake Chen; Souvik Kundu 002; Haomei Liu; Ruiheng Peng; Peter A. Beerel (DAC, 2023)

Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M). - Md. Abdullah-Al Kaiser; Gourav Datta; Sreetama Sarkar; Souvik Kundu 002; Zihan Yin; Manas Garg; Ajey P. Jacob; Peter A. Beerel; Akhilesh R. Jaiswal (ACM Great Lakes Symposium on VLSI, 2023)

In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision. - Gourav Datta; Zeyu Liu 003; Md. Abdullah-Al Kaiser; Souvik Kundu 002; Joe Mathai; Zihan Yin; Ajey P. Jacob; Akhilesh R. Jaiswal; Peter A. Beerel (ICASSP, 2023)

Sparse Mixture Once-for-all Adversarial Training for Efficient in-situ Trade-off between Accuracy and Robustness of DNNs. - Souvik Kundu 002; Sairam Sundaresan; Sharath Nittur Sridhar; Shunlin Lu; Han Tang; Peter A. Beerel (ICASSP, 2023)

Quantpipe: Applying Adaptive Post-Training Quantization For Distributed Transformer Pipelines In Dynamic Edge Environments. - Haonan Wang; Connor Imes; Souvik Kundu 002; Peter A. Beerel; Stephen P. Crago; John Paul Walters (ICASSP, 2023)

RNA-ViT: Reduced-Dimension Approximate Normalized Attention Vision Transformers for Latency Efficient Private Inference. - Dake Chen; Yuke Zhang; Souvik Kundu 002; Chenghao Li; Peter A. Beerel (ICCAD, 2023)

SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation. - Yuke Zhang; Dake Chen; Souvik Kundu 002; Chenghao Li; Peter A. Beerel (ICCV, 2023)

FireFly: A Synthetic Dataset for Ember Detection in Wildfire. - Yue Hu; Xinan Ye; Yifei Liu; Souvik Kundu 002; Gourav Datta; Srikar Mutnuri; Namo Asavisanu; Nora Ayanian; Konstantinos Psounis; Peter A. Beerel (ICCV (Workshops), 2023)

InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning. - Sharath Nittur Sridhar; Souvik Kundu 002; Sairam Sundaresan; Maciej Szankin; Anthony Sarah (ICCV (Workshops), 2023)

Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference. - Souvik Kundu 002; Shunlin Lu; Yuke Zhang; Jacqueline Tiffany Liu; Peter A. Beerel (ICLR, 2023)

ViTA: A Vision Transformer Inference Accelerator for Edge Applications. - Shashank Nag; Gourav Datta; Souvik Kundu 002; Nitin Chandrachoodan; Peter A. Beerel (ISCAS, 2023)

Self-Attentive Pooling for Efficient Deep Learning. - Fang Chen; Gourav Datta; Souvik Kundu 002; Peter A. Beerel (WACV, 2023)

FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-off between Accuracy and Robustness. - Souvik Kundu 002; Sairam Sundaresan; Massoud Pedram; Peter A. Beerel (WACV, 2023)

C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference. - Yuke Zhang; Dake Chen; Souvik Kundu 002; Haomei Liu; Ruiheng Peng; Peter A. Beerel (CoRR, 2023)

Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT. - Souvik Kundu 002; Sharath Nittur Sridhar; Maciej Szankin; Sairam Sundaresan (CoRR, 2023)

Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? - Sara Babakniya; Souvik Kundu 002; Saurav Prakash; Yue Niu; Salman Avestimehr (Trans. Mach. Learn. Res., 2023)

Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients. - Yue Niu; Saurav Prakash; Souvik Kundu 002; Sunwoo Lee 001; Salman Avestimehr (Trans. Mach. Learn. Res., 2023)

AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models. - Zeyu Liu 003; Souvik Kundu 002; Anni Li; Junrui Wan; Lianghao Jiang; Peter A. Beerel (ACL (Short Papers), 2024)

DIA: Diffusion based Inverse Network Attack on Collaborative Inference. - Dake Chen; Shiduo Li; Yuke Zhang; Chenghao Li; Souvik Kundu 002; Peter A. Beerel (CVPR Workshops, 2024)

RLNet: Robust Linearized Networks for Efficient Private Inference. - Sreetama Sarkar; Souvik Kundu 002; Peter A. Beerel (CVPR Workshops, 2024)

Block Selective Reprogramming for On-device Training of Vision Transformers. - Sreetama Sarkar; Souvik Kundu 002; Kai Zheng; Peter A. Beerel (CVPR Workshops, 2024)

CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-training Quantization of ViTs. - Akshat Ramachandran; Souvik Kundu 002; Tushar Krishna (ECCV (67), 2024)

LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation. - Seyedarmin Azizi; Souvik Kundu 002; Massoud Pedram (EMNLP (Findings), 2024)

Sensi-Bert: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient Language Model. - Souvik Kundu 002; Sharath Nittur Sridhar; Maciej Szankin; Sairam Sundaresan (ICASSP, 2024)

Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural Networks: from Algorithms to Technology. - Souvik Kundu 002; Rui-Jie Zhu 003; Akhilesh Jaiswal 001; Peter A. Beerel (ICASSP, 2024)

What Makes Vision Transformers Robust Towards Bit-Flip Attack? - Xuan Zhou; Souvik Kundu 002; Dake Chen; Jie Huang; Peter A. Beerel (ICPR (8), 2024)

Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. - Yuchen Xia; Jiho Kim; Yuhan Chen; Haojie Ye; Souvik Kundu 002; Cong Callie Hao; Nishil Talati (IISWC, 2024)

Linearizing Models for Efficient yet Robust Private Inference. - Sreetama Sarkar; Souvik Kundu 002; Peter A. Beerel (CoRR, 2024)

CiMNet: Towards Joint Optimization for DNN Architecture and Configuration for Compute-In-Memory Hardware. - Souvik Kundu 002; Anthony Sarah; Vinay Joshi; Om Ji Omer; Sreenivas Subramoney (CoRR, 2024)

MambaExtend: A Training-Free Approach to Improve Long Context Extension of Mamba. - Seyedarmin Azizi; Souvik Kundu 002; Mohammad Erfan Sadeghi; Massoud Pedram (ICLR, 2025)

LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression. - Souvik Kundu 002; Anahita Bhiwandiwalla; Sungduk Yu; Phillip Howard; Tiep Le; Sharath Nittur Sridhar; David Cobbley; Hao Kang; Vasudev Lal (NAACL (Findings), 2025)

MaskVD: Region Masking for Efficient Video Object Detection. - Sreetama Sarkar; Gourav Datta; Souvik Kundu 002; Kai Zheng; Chirayata Bhattacharyya; Peter A. Beerel (WACV, 2025)

MoDM: Efficient Serving for Image Generation via Mixture-of-Diffusion Models. - Yuchen Xia; Divyam Sharma; Yichao Yuan; Souvik Kundu 002; Nishil Talati (CoRR, 2025)

Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression. - Sreetama Sarkar; Yue Che; Alex Gavin; Peter A. Beerel; Souvik Kundu 002 (CoRR, 2025)