| Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing S Tuli, S Tuli, R Tuli, SS Gill Internet of things 11, 100222, 2020 | 516 | 2020 |
| Transformative effects of IoT, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges SS Gill, S Tuli, M Xu, I Singh, KV Singh, D Lindsay, S Tuli, D Smirnova, ... Internet of Things, 100118, 2019 | 424 | 2019 |
| Fogbus: A blockchain-based lightweight framework for edge and fog computing S Tuli, R Mahmud, S Tuli, R Buyya Journal of Systems and Software 154, 22-36, 2019 | 376 | 2019 |
| Are Convolutional Neural Networks or Transformers more like human vision? S Tuli, I Dasgupta, E Grant, TL Griffiths Annual Meeting of the Cognitive Science Society (CogSci), 2021 | 195 | 2021 |
| Next Generation Technologies for Smart Healthcare: Challenges, Vision, Model, Trends and Future Directions S Tuli, S Tuli, G Wander, P Wander, SS Gill, S Dustdar, R Sakellariou, ... Internet Technology Letters, 2019 | 110 | 2019 |
| Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11, 100222 S Tuli, S Tuli, R Tuli, SS Gill | 23 | 2020 |
| Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118 SS Gill, S Tuli, M Xu, I Singh, KV Singh, D Lindsay, S Tuli, D Smirnova, ... | 22 | 2019 |
| Modelling for prediction of the spread and severity of COVID-19 and its association with socioeconomic factors and virus types S Tuli, S Tuli, R Verma, R Tuli MedRxiv, 2020.06. 18.20134874, 2020 | 21 | 2020 |
| & Garraghan, P.(2019) SS Gill, S Tuli, M Xu, I Singh, KV Singh, D Lindsay Transformative effects of IoT, Blockchain and Artificial Intelligence on …, 2019 | 20* | 2019 |
| FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid? S Tuli, B Dedhia, S Tuli, NK Jha Journal of Artificial Intelligence Research 77, 39-70, 2023 | 12 | 2023 |
| AccelTran: A sparsity-aware accelerator for dynamic inference with transformers S Tuli, NK Jha IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023 | 12 | 2023 |
| DHOOM: Reusing Design-for-Debug Hardware for Online Monitoring N Jindal, S Chandran, PR Panda, S Prasad, A Mitra, K Singhal, S Gupta, ... Design Automation Conference 2019, 99, 2019 | 11 | 2019 |
| CODEBench: A neural architecture and hardware accelerator co-design framework S Tuli, CH Li, R Sharma, NK Jha ACM Transactions on Embedded Computing Systems 22 (3), 1-30, 2023 | 9 | 2023 |
| RRAM-VAC: A Variability-Aware Controller for RRAM-based Memory Architectures S Tuli, MA Rios, ASJ Levisse, D Atienza Alonso Asia and South Pacific Design Automation Conference ASP-DAC 2020, 2020 | 9 | 2020 |
| Generative Optimization Networks for Memory Efficient Data Generation S Tuli, S Tuli, G Casale, NR Jennings Advances in Neural Information Processing Systems (NeurIPS), Workshop on ML …, 2021 | 8 | 2021 |
| Characterization and modeling of hot carrier degradation in N-channel gate-all-around nanowire FETs C Gupta, A Gupta, S Tuli, E Bury, B Parvais, A Dixit IEEE Transactions on Electron Devices 67 (1), 4-10, 2019 | 7 | 2019 |
| EdgeTran: Co-designing transformers for efficient inference on mobile edge platforms S Tuli, NK Jha arXiv preprint arXiv:2303.13745, 2023 | 4 | 2023 |
| Design of a conventional-transistor-based analog integrated circuit for on-chip learning in a spiking neural network S Tuli, D Bhowmik International Conference on Neuromorphic Systems 2020, 1-8, 2020 | 4 | 2020 |
| AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices S Tuli, S Tuli IEEE International Symposium on Circuits and Systems, 2020 | 4 | 2020 |
| TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference S Tuli, NK Jha IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023 | 3 | 2023 |
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