| Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks S Jain, A Gural, M Wu, C Dick Proceedings of Machine Learning and Systems 2, 112-128, 2020 | 147 | 2020 |
| Trained uniform quantization for accurate and efficient neural network inference on fixed-point hardware SR Jain, A Gural, M Wu, C Dick arXiv preprint arXiv:1903.08066 6 (6), 3, 2019 | 56 | 2019 |
| CHIMERA: A 0.92 TOPS, 2.2 TOPS/W edge AI accelerator with 2 MByte on-chip foundry resistive RAM for efficient training and inference M Giordano, K Prabhu, K Koul, RM Radway, A Gural, R Doshi, ZF Khan, ... 2021 symposium on VLSI circuits, 1-2, 2021 | 49 | 2021 |
| Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications. A Gural, B Murmann ICML, 2515-2524, 2019 | 33 | 2019 |
| CHIMERA: A 0.92-TOPS, 2.2-TOPS/W edge AI accelerator with 2-MByte on-chip foundry resistive RAM for efficient training and inference K Prabhu, A Gural, ZF Khan, RM Radway, M Giordano, K Koul, R Doshi, ... IEEE Journal of Solid-State Circuits 57 (4), 1013-1026, 2022 | 29 | 2022 |
| A 4μW, ADPLL-based implantable amperometric biosensor in 65nm CMOS A Agarwal, A Gural, M Monge, D Adalian, S Chen, A Scherer, A Emami 2017 Symposium on VLSI Circuits, C108-C109, 2017 | 23 | 2017 |
| RRAM-based in-memory computing for embedded deep neural networks D Bankman, J Messner, A Gural, B Murmann 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1511-1515, 2019 | 12 | 2019 |
| Low-rank training of deep neural networks for emerging memory technology A Gural, P Nadeau, M Tikekar, B Murmann arXiv preprint arXiv:2009.03887, 2020 | 7 | 2020 |
| Algorithmic Techniques for Neural Network Training on Memory-Constrained Hardware A Gural Stanford University, 2021 | 1 | 2021 |
| Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Devices A Gural | 1 | 2019 |
| System and method for implementing neural networks in integrated circuits AT Gural, SR Jain, CH Dick US Patent 11,586,908, 2023 | | 2023 |
| Method and system for convolution AT Gural, M Wu, CH Dick US Patent 11,580,191, 2023 | | 2023 |
| Trained Quantization Thresholds (TQT) S Jain | | 2020 |
