| RaPiD: AI Accelerator for Ultra-low Precision Training and Inference S Venkataramani, V Srinivasan, W Wang, S Sen, J Zhang, A Agrawal, ... 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture …, 2021 | 122 | 2021 |
| EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks S Sen, B Ravindran, A Raghunathan 2020 International Conference on Learning Representations (ICLR), 2020 | 90 | 2020 |
| Approximate computing for spiking neural networks S Sen, S Venkataramani, A Raghunathan 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE), 193-198, 2017 | 83 | 2017 |
| Approximate Computing for Long Short Term Memory (LSTM) Neural Networks S Sen, A Raghunathan IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2018 | 68 | 2018 |
| SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks S Sen, S Jain, S Venkataramani, A Raghunathan IEEE Transactions on Computers 68 (6), 912-925, 2018 | 62 | 2018 |
| A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks A Roy, S Venkataramani, N Gala, S Sen, K Veezhinathan, ... 2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017 | 35 | 2017 |
| Deep Compression of Pre-trained Transformer Models N Wang, CCC Liu, S Venkataramani, S Sen, CY Chen, K El Maghraoui, ... Advances in Neural Information Processing Systems 35, 14140-14154, 2022 | 31 | 2022 |
| Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks S Krithivasan, S Sen, S Venkataramani, A Raghunathan 2019 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2019 | 30 | 2019 |
| Efficiency attacks on spiking neural networks S Krithivasan, S Sen, N Rathi, K Roy, A Raghunathan Proceedings of the 59th ACM/IEEE Design Automation Conference, 373-378, 2022 | 19 | 2022 |
| Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks S Krithivasan, S Sen, A Raghunathan IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2020 | 16 | 2020 |
| Accelerating DNN Training Through Selective Localized Learning S Krithivasan, S Sen, S Venkataramani, A Raghunathan Frontiers in Neuroscience 15, 759807, 2022 | 11 | 2022 |
| Optimizing transformers with approximate computing for faster, smaller and more accurate NLP models A Nagarajan, S Sen, JR Stevens, A Raghunathan | 9 | 2020 |
| Value Similarity Extensions for Approximate Computing in General-Purpose Processors Y Kim, S Venkataramani, S Sen, A Raghunathan 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), 481-486, 2021 | 8 | 2021 |
| A Case for Generalizable DNN Cost Models for Mobile Devices V Ganesan, S Selvam, S Sen, P Kumar, A Raghunathan 2020 IEEE International Symposium on Workload Characterization (IISWC), 169-180, 2020 | 7 | 2020 |
| Sparsity-Aware Caches to Accelerate Deep Neural Networks V Ganesan, S Sen, P Kumar, N Gala, K Veezhinathan, A Raghunathan 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 85-90, 2020 | 7 | 2020 |
| SmartQuant: CXL-Based AI Model Store in Support of Runtime Configurable Weight Quantization R Xie, AU Haq, L Ma, K Sun, S Sen, S Venkataramani, L Liu, T Zhang IEEE Computer Architecture Letters, 2024 | 6 | 2024 |
| Probabilistic spike propagation for efficient hardware implementation of spiking neural networks A Nallathambi, S Sen, A Raghunathan, N Chandrachoodan Frontiers in Neuroscience 15, 694402, 2021 | 6 | 2021 |
| 14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC M Kar, J Silberman, S Venkataramani, V Srinivasan, B Fleischer, J Rubin, ... 2024 IEEE International Solid-State Circuits Conference (ISSCC) 67, 254-256, 2024 | 4 | 2024 |
| AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models A Nagarajan, S Sen, JR Stevens, A Raghunathan 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 4 | 2022 |
| RaPiD: AI Accelerator for Ultra-low Precision Training and Inference. In 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA). 153–166 S Venkataramani, V Srinivasan, W Wang, S Sen, J Zhang, A Agrawal, ... Google Scholar Google Scholar Digital Library Digital Library, 2021 | 4 | 2021 |
Anand RaghunathanSilicon Valley Chair Professor of Electrical and Computer Engineering, Purdue UniversityVerified email at purdue.edu
