| Resistive crossbars as approximate hardware building blocks for machine learning: Opportunities and challenges I Chakraborty, M Ali, A Ankit, S Jain, S Roy, S Sridharan, A Agrawal, ... Proceedings of the IEEE 108 (12), 2276-2310, 2020 | 84 | 2020 |
| Proposal for a leaky-integrate-fire spiking neuron based on magnetoelectric switching of ferromagnets A Jaiswal, S Roy, G Srinivasan, K Roy IEEE Transactions on Electron Devices 64 (4), 1818-1824, 2017 | 81 | 2017 |
| TxSim: Modeling training of deep neural networks on resistive crossbar systems S Roy, S Sridharan, S Jain, A Raghunathan IEEE Transactions on Very Large Scale Integration (VLSI) Systems 29 (4), 730-738, 2021 | 43 | 2021 |
| Pruning filters while training for efficiently optimizing deep learning networks S Roy, P Panda, G Srinivasan, A Raghunathan 2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020 | 31 | 2020 |
| Spike timing dependent plasticity based enhanced self-learning for efficient pattern recognition in spiking neural networks G Srinivasan, S Roy, V Raghunathan, K Roy 2017 International Joint Conference on Neural Networks (IJCNN), 1847-1854, 2017 | 28 | 2017 |
| Gradual channel pruning while training using feature relevance scores for convolutional neural networks SA Aketi, S Roy, A Raghunathan, K Roy IEEE Access 8, 171924-171932, 2020 | 27 | 2020 |
| Compute-in-memory technologies and architectures for deep learning workloads M Ali, S Roy, U Saxena, T Sharma, A Raghunathan, K Roy IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30 (11 …, 2022 | 14 | 2022 |
| PIM-DRAM: Accelerating machine learning workloads using processing in commodity DRAM S Roy, M Ali, A Raghunathan IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11 (4 …, 2021 | 13 | 2021 |
| Design tools for resistive crossbar based machine learning accelerators I Chakraborty, S Roy, S Sridharan, M Ali, A Ankit, S Jain, A Raghunathan 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits …, 2021 | 2 | 2021 |
| Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators S Roy, C Wang, A Raghunathan arXiv preprint arXiv:2308.02024, 2023 | | 2023 |
| Brain Inspired Enhanced Learning Mechanism Based on Spike Timing Dependent Plasticity (STDP) for Efficient Pattern Recognition in Spiking Neural Networks S Roy, G Srinivasan, V Raghunathan | | 2016 |
Anand RaghunathanSilicon Valley Chair Professor of Electrical and Computer Engineering, Purdue University在 purdue.edu 的电子邮件经过验证
