| Computing in Memory with Spin-Transfer Torque Magnetic RAM S Jain, A Ranjan, K Roy, A Raghunathan IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26 (3), 470-483, 2018 | 291 | 2018 |
| RxNN: A framework for evaluating deep neural networks on resistive crossbars S Jain, A Sengupta, K Roy, A Raghunathan IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020 | 88* | 2020 |
| Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errors S Jain, S Venkataramani, V Srinivasan, J Choi, P Chuang, L Chang Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018 | 59 | 2018 |
| 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 | 47 | 2020 |
| 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 | 39 | 2018 |
| 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 | 36 | 2021 |
| Cxdnn: Hardware-software compensation methods for deep neural networks on resistive crossbar systems S Jain, A Raghunathan ACM Transactions on Embedded Computing Systems (TECS) 18 (6), 1-23, 2019 | 36 | 2019 |
| Efficient AI system design with cross-layer approximate computing S Venkataramani, X Sun, N Wang, CY Chen, J Choi, M Kang, A Agarwal, ... Proceedings of the IEEE 108 (12), 2232-2250, 2020 | 33 | 2020 |
| 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 | 31 | 2021 |
| X-mann: A crossbar based architecture for memory augmented neural networks A Ranjan, S Jain, JR Stevens, D Das, B Kaul, A Raghunathan Proceedings of the 56th Annual Design Automation Conference 2019, 1-6, 2019 | 30 | 2019 |
| BiScaled-DNN: Quantizing long-tailed datastructures with two scale factors for deep neural networks S Jain, S Venkataramani, V Srinivasan, J Choi, K Gopalakrishnan, ... Proceedings of the 56th Annual Design Automation Conference 2019, 1-6, 2019 | 30 | 2019 |
| TiM-DNN: Ternary in-memory accelerator for deep neural networks S Jain, SK Gupta, A Raghunathan IEEE Transactions on Very Large Scale Integration (VLSI) Systems 28 (7 …, 2020 | 25 | 2020 |
| System and method for in-memory computing S Jain, A Ranjan, K Roy, A Raghunathan US Patent 10,073,733, 2018 | 24 | 2018 |
| Approximation through logic isolation for the design of quality configurable circuits S Jain, S Venkataramani, A Raghunathan 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), 612-617, 2016 | 21 | 2016 |
| Computing-in-memory with spintronics S Jain, S Sapatnekar, JP Wang, K Roy, A Raghunathan 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2018 | 20 | 2018 |
| Non-volatile memory utilizing reconfigurable ferroelectric transistors to enable differential read and energy-efficient in-memory computation SK Thirumala, S Jain, A Raghunathan, SK Gupta 2019 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2019 | 17 | 2019 |
| Neural network accelerator design with resistive crossbars: Opportunities and challenges S Jain, A Ankit, I Chakraborty, T Gokmen, M Rasch, W Haensch, K Roy, ... IBM Journal of Research and Development 63 (6), 10: 1-10: 13, 2019 | 16 | 2019 |
| Valley-coupled-spintronic non-volatile memories with compute-in-memory support SK Thirumala, YT Hung, S Jain, A Raha, N Thakuria, V Raghunathan, ... IEEE Transactions on Nanotechnology 19, 635-647, 2020 | 7 | 2020 |
| Ax-BxP: Approximate blocked computation for precision-reconfigurable deep neural network Acceleration R Elangovan, S Jain, A Raghunathan ACM Transactions on Design Automation of Electronic Systems (TODAES) 27 (3 …, 2022 | 6 | 2022 |
| Ternary compute-enabled memory using ferroelectric transistors for accelerating deep neural networks SK Thirumala, S Jain, SK Gupta, A Raghunathan 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 31-36, 2020 | 5 | 2020 |
Anand RaghunathanProfessor of Electrical and Computer Engineering, Purdue UniversityVerified email at purdue.edu
