| 2026 | DATE | MD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural Network. | Donghyun Lee, Abhishek Moitra, Youngeun Kim, Ruokai Yin, Priyadarshini Panda |
| 2025 | CVPR | Spiking Transformer with Spatial-Temporal Attention. | Donghyun Lee, Yuhang Li, Youngeun Kim, Shiting Xiao, Priyadarshini Panda |
| 2025 | DAC | FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision. | Jingxiao Ma, Priyadarshini Panda, Sherief Reda |
| 2025 | DAC | PacQ: A SIMT Microarchitecture for Efficient Dataflow in Hyper-asymmetric GEMMs. | Ruokai Yin, Yuhang Li, Priyadarshini Panda |
| 2025 | DATE | Rhychee-FL: Robust and Efficient Hyperdimensional Federated Learning with Homomorphic Encryption. | Yujin Nam, Abhishek Moitra, Yeshwanth Venkatesha, Xiaofan Yu, Gabrielle De Micheli, Xuan Wang, Minxuan Zhou, Augusto Vega, Priyadarshini Panda, Tajana Rosing |
| 2025 | DATE | Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges. | Amit Ranjan Trivedi, Sina Tayebati, Hemant Kumawat, Nastaran Darabi, Divake Kumar, Adarsh Kumar Kosta, Yeshwanth Venkatesha, Dinithi Jayasuriya, Nethmi Jayasinghe, Priyadarshini Panda, Saibal Mukhopadhyay, Kaushik Roy |
| 2025 | ICML | GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration. | Yuhang Li, Ruokai Yin, Donghyun Lee, Shiting Xiao, Priyadarshini Panda |
| 2025 | ISCAS | Low-power Spike-based Wearable Analytics on RRAM Crossbars. | Abhiroop Bhattacharjee, Jinquan Shi, Wei-Chen Chen, Xinxin Wang, Priyadarshini Panda |
| 2025 | ISLPED | SITRA: Exploiting Temporal Silence in Spiking Transformers for Fast & Energy-efficient Inference. | Abhiroop Bhattacharjee, Abhishek Moitra, Ruokai Yin, Priyadarshini Panda |
| 2025 | ISLPED | Tutorial: Autonomy with Neuromorphic System. | Amit Ranjan Trivedi, Priyadarshini Panda, Kaushik Roy, Saibal Mukhopadhyay |
| 2024 | ASPDAC | MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks. | Ruokai Yin, Yuhang Li, Abhishek Moitra, Priyadarshini Panda |
| 2024 | DAC | PIVOT- Input-aware Path Selection for Energy-efficient ViT Inference. | Abhishek Moitra, Abhiroop Bhattacharjee, Priyadarshini Panda |
| 2024 | DATE | TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training. | Donghyun Lee, Ruokai Yin, Youngeun Kim, Abhishek Moitra, Yuhang Li, Priyadarshini Panda |
| 2024 | DATE | HaLo-FL: Hardware-Aware Low-Precision Federated Learning. | Yeshwanth Venkatesha, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda |
| 2024 | ECCV | One-Stage Prompt-Based Continual Learning. | Youngeun Kim, Yuhang Li, Priyadarshini Panda |
| 2024 | ECCV | GenQ: Quantization in Low Data Regimes with Generative Synthetic Data. | Yuhang Li, Youngeun Kim, Donghyun Lee, Souvik Kundu, Priyadarshini Panda |
| 2024 | ICASSP | Are SNNs Truly Energy-efficient? - A Hardware Perspective. | Abhiroop Bhattacharjee, Ruokai Yin, Abhishek Moitra, Priyadarshini Panda |
| 2024 | MICRO | LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks. | Ruokai Yin, Youngeun Kim, Di Wu, Priyadarshini Panda |
| 2023 | AAAI | Exploring Temporal Information Dynamics in Spiking Neural Networks. | Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Anna Hambitzer, Priyadarshini Panda |
| 2023 | ACSSC | Energy-efficient Hardware Design for Spiking Neural Networks (Extended Abstract). | Abhishek Moitra, Ruokai Yin, Priyadarshini Panda |
| 2023 | DAC | Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing. | Yuhang Li, Abhishek Moitra, Tamar Geller, Priyadarshini Panda |
| 2023 | DAC | XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing. | Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim, Priyadarshini Panda |
| 2023 | DATE | DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks. | Duy-Thanh Nguyen, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda |
| 2022 | AAAI | PrivateSNN: Privacy-Preserving Spiking Neural Networks. | Youngeun Kim, Yeshwanth Venkatesha, Priyadarshini Panda |
| 2022 | DAC | MIME: adapting a single neural network for multi-task inference with memory-efficient dynamic pruning. | Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Abhishek Moitra, Priyadarshini Panda |
| 2022 | DATE | Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks. | Abhiroop Bhattacharjee, Lakshya Bhatnagar, Priyadarshini Panda |
| 2022 | DATE | Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar. | Youngeun Kim, Hyunsoo Kim, Seijoon Kim, Sang Joon Kim, Priyadarshini Panda |
| 2022 | ECCV | Neural Architecture Search for Spiking Neural Networks. | Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Priyadarshini Panda |
| 2022 | ECCV | Exploring Lottery Ticket Hypothesis in Spiking Neural Networks. | Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Ruokai Yin, Priyadarshini Panda |
| 2022 | ECCV | Neuromorphic Data Augmentation for Training Spiking Neural Networks. | Yuhang Li, Youngeun Kim, Hyoungseob Park, Tamar Geller, Priyadarshini Panda |
| 2022 | ICASSP | Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks? | Youngeun Kim, Hyoungseob Park, Abhishek Moitra, Abhiroop Bhattacharjee, Yeshwanth Venkatesha, Priyadarshini Panda |
| 2022 | ICRA | RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning. | Adarsh Kumar Kosta, Malik Aqeel Anwar, Priyadarshini Panda, Arijit Raychowdhury, Kaushik Roy |
| 2022 | ISLPED | Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars. | Abhiroop Bhattacharjee, Youngeun Kim, Abhishek Moitra, Priyadarshini Panda |
| 2021 | DATE | Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks. | Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda |
| 2021 | DATE | Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks. | Karina Vasquez, Yeshwanth Venkatesha, Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda |
| 2020 | ECCV | Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-linear Activations. | Saima Sharmin, Nitin Rathi, Priyadarshini Panda, Kaushik Roy |
| 2020 | ICASSP | Training Deep Spiking Neural Networks for Energy-Efficient Neuromorphic Computing. | Gopalakrishnan Srinivasan, Chankyu Lee, Abhronil Sengupta, Priyadarshini Panda, Syed Shakib Sarwar, Kaushik Roy |
| 2020 | ICLR | Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation. | Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy |
| 2020 | ICPR | Activation Density Driven Efficient Pruning in Training. | Timothy Foldy-Porto, Yeshwanth Venkatesha, Priyadarshini Panda |
| 2020 | IJCNN | Energy-efficient and Robust Cumulative Training with Net2Net Transformation. | Aosong Feng, Priyadarshini Panda |
| 2020 | IJCNN | Enabling Homeostasis using Temporal Decay Mechanisms in Spiking CNNs Trained with Unsupervised Spike Timing Dependent Plasticity. | Krishna Reddy Kesari, Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy |
| 2020 | IJCNN | Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks. | Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan, Anand Raghunathan |
| 2020 | ISLPED | QUANOS: adversarial noise sensitivity driven hybrid quantization of neural networks. | Priyadarshini Panda |
| 2020 | VLSID | Invited Talk: Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits, and Systems. | Priyadarshini Panda, Kaushik Roy |
| 2019 | IJCNN | Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra Analysis. | Priyadarshini Panda, Efstathia Soufleri, Kaushik Roy |
| 2019 | IJCNN | A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks. | Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar, Chankyu Lee, Wachirawit Ponghiran, Kaushik Roy |
| 2019 | SmartComp | Neural Networks at the Edge. | Deboleena Roy, Gopalakrishnan Srinivasan, Priyadarshini Panda, Richard Tomsett, Nirmit Desai, Raghu K. Ganti, Kaushik Roy |
| 2017 | DAC | RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks. | Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda, Kaushik Roy |
| 2017 | DATE | Semantic driven hierarchical learning for energy-efficient image classification. | Priyadarshini Panda, Kaushik Roy |
| 2017 | IJCNN | EnsembleSNN: Distributed assistive STDP learning for energy-efficient recognition in spiking neural networks. | Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy |
| 2017 | ISLPED | Gabor filter assisted energy efficient fast learning Convolutional Neural Networks. | Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy |
| 2016 | DAC | Invited - Cross-layer approximations for neuromorphic computing: from devices to circuits and systems. | Priyadarshini Panda, Abhronil Sengupta, Syed Shakib Sarwar, Gopalakrishnan Srinivasan, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy |
| 2016 | DATE | Conditional Deep Learning for energy-efficient and enhanced pattern recognition. | Priyadarshini Panda, Abhronil Sengupta, Kaushik Roy |
| 2016 | IJCNN | Unsupervised regenerative learning of hierarchical features in Spiking Deep Networks for object recognition. | Priyadarshini Panda, Kaushik Roy |
| 2016 | VLSID | Neuromorphic Computing Enabled by Spin-Transfer Torque Devices. | Abhronil Sengupta, Priyadarshini Panda, Anand Raghunathan, Kaushik Roy |