Understanding the impact of precision quantization on the accuracy and energy of neural networks S Hashemi, N Anthony, H Tann, RI Bahar, S Reda Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 …, 2017 | 164 | 2017 |
Runtime configurable deep neural networks for energy-accuracy trade-off H Tann, S Hashemi, RI Bahar, S Reda Proceedings of the eleventh ieee/acm/ifip international conference on …, 2016 | 91 | 2016 |
Hardware-software codesign of accurate, multiplier-free deep neural networks H Tann, S Hashemi, RI Bahar, S Reda Proceedings of the 54th Annual Design Automation Conference 2017, 1-6, 2017 | 81 | 2017 |
BLASYS: Approximate logic synthesis using Boolean matrix factorization S Hashemi, H Tann, S Reda Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018 | 73 | 2018 |
Automated high-level generation of low-power approximate computing circuits K Nepal, S Hashemi, H Tann, RI Bahar, S Reda IEEE Transactions on Emerging Topics in Computing 7 (1), 18-30, 2016 | 73 | 2016 |
Approximate computing for biometric security systems: A case study on iris scanning S Hashemi, H Tann, F Buttafuoco, S Reda 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 319-324, 2018 | 27 | 2018 |
Principles of information storage in small-molecule mixtures JK Rosenstein, C Rose, S Reda, PM Weber, E Kim, J Sello, J Geiser, ... IEEE Transactions on NanoBioscience 19 (3), 378-384, 2020 | 24 | 2020 |
Flexible deep neural network processing H Tann, S Hashemi, S Reda arXiv preprint arXiv:1801.07353, 2018 | 15 | 2018 |
UDC: Unified DNAS for compressible TinyML models I Fedorov, R Matas, H Tann, C Zhou, M Mattina, P Whatmough arXiv preprint arXiv:2201.05842, 2022 | 13 | 2022 |
A resource-efficient embedded iris recognition system using fully convolutional networks H Tann, H Zhao, S Reda ACM Journal on Emerging Technologies in Computing Systems (JETC) 16 (1), 1-23, 2019 | 9 | 2019 |
Udc: Unified dnas for compressible tinyml models for neural processing units I Fedorov, R Matas, H Tann, C Zhou, M Mattina, P Whatmough Advances in Neural Information Processing Systems 35, 18456-18471, 2022 | 8 | 2022 |
Parallelized linear classification with volumetric chemical perceptrons CE Arcadia, H Tann, A Dombroski, K Ferguson, SL Chen, E Kim, C Rose, ... 2018 IEEE International Conference on Rebooting Computing (ICRC), 1-9, 2018 | 8 | 2018 |
Leveraging autocatalytic reactions for chemical domain image classification CE Arcadia, A Dombroski, K Oakley, SL Chen, H Tann, C Rose, E Kim, ... Chemical Science 12 (15), 5464-5472, 2021 | 7 | 2021 |
Approximate logic synthesis using Boolean matrix factorization S Hashemi, H Tann, S Reda Approximate Circuits: Methodologies and CAD, 141-154, 2019 | 7 | 2019 |
Lightweight Deep Neural Network Accelerators Using Approximate SW/HW Techniques H Tann, S Hashemi, S Reda Approximate Circuits, 289-305, 2019 | 7 | 2019 |
Approximate computing for iris recognition systems H Tann, S Hashemi, F Buttafuoco, S Reda Approximate Circuits: Methodologies and CAD, 331-348, 2019 | 5 | 2019 |
New vision system and navigation algorithm for an autonomous ground vehicle H Tann, B Shakya, AC Merchen, BC Williams, A Khanal, J Zhao, ... Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques 9025 …, 2014 | 2 | 2014 |
Methods of chemical computation B Rubenstein, JK Rosenstein, C Arcadia, SL Chen, AD Dombroski, ... US Patent 11,093,865, 2021 | 1 | 2021 |
Improved obstacle avoidance and navigation for an autonomous ground vehicle B Giri, H Cho, BC Williams, H Tann, B Shakya, V Bharam, DJ Ahlgren Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques 9406 …, 2015 | 1 | 2015 |
System, devices and/or processes for defining a search space for neural network processing device architectures H Tann, RM Navarro, I Fedorov, C Zhou, PN Whatmough, M Mattina US Patent App. 17/817,142, 2024 | | 2024 |