Deep learning with limited numerical precision S Gupta, A Agrawal, K Gopalakrishnan, P Narayanan
International conference on machine learning, 1737-1746, 2015
2751 2015 Phase change memory technology GW Burr, MJ Breitwisch, M Franceschini, D Garetto, K Gopalakrishnan, ...
Journal of Vacuum Science & Technology B: Microelectronics and Nanometer …, 2010
1302 2010 Overview of candidate device technologies for storage-class memory GW Burr, BN Kurdi, JC Scott, CH Lam, K Gopalakrishnan, RS Shenoy
IBM Journal of Research and Development 52 (4.5), 449-464, 2008
1164 2008 Pact: Parameterized clipping activation for quantized neural networks J Choi, Z Wang, S Venkataramani, PIJ Chuang, V Srinivasan, ...
arXiv preprint arXiv:1805.06085, 2018
1150 2018 Training deep neural networks with 8-bit floating point numbers N Wang, J Choi, D Brand, CY Chen, K Gopalakrishnan
Advances in neural information processing systems 31, 2018
648 2018 I-MOS: A novel semiconductor device with a subthreshold slope lower than kT/q K Gopalakrishnan, PB Griffin, JD Plummer
Digest. International Electron Devices Meeting,, 289-292, 2002
467 2002 Activation and diffusion studies of ion-implanted CO Chui, K Gopalakrishnan, PB Griffin, JD Plummer, KC Saraswat
Applied physics letters 83 (16), 3275-3277, 2003
377 2003 Impact ionization MOS (I-MOS)-Part I: device and circuit simulations K Gopalakrishnan, PB Griffin, JD Plummer
IEEE Transactions on electron devices 52 (1), 69-76, 2004
361 2004 Hybrid 8-bit floating point (HFP8) training and inference for deep neural networks X Sun, J Choi, CY Chen, N Wang, S Venkataramani, VV Srinivasan, X Cui, ...
Advances in neural information processing systems 32, 2019
268 2019 Ultra-low precision 4-bit training of deep neural networks X Sun, N Wang, CY Chen, J Ni, A Agrawal, X Cui, S Venkataramani, ...
Advances in Neural Information Processing Systems 33, 1796-1807, 2020
231 2020 Accurate and efficient 2-bit quantized neural networks J Choi, S Venkataramani, VV Srinivasan, K Gopalakrishnan, Z Wang, ...
Proceedings of Machine Learning and Systems 1, 348-359, 2019
212 2019 Adacomp: Adaptive residual gradient compression for data-parallel distributed training CY Chen, J Choi, D Brand, A Agrawal, W Zhang, K Gopalakrishnan
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
212 2018 Nanoscale electronic synapses using phase change devices BL Jackson, B Rajendran, GS Corrado, M Breitwisch, GW Burr, R Cheek, ...
ACM Journal on Emerging Technologies in Computing Systems (JETC) 9 (2), 1-20, 2013
203 2013 Specifications of nanoscale devices and circuits for neuromorphic computational systems B Rajendran, Y Liu, J Seo, K Gopalakrishnan, L Chang, DJ Friedman, ...
IEEE Transactions on Electron Devices 60 (1), 246-253, 2012
197 2012 Impact ionization MOS (I-MOS)-part II: experimental results K Gopalakrishnan, R Woo, C Jungemann, PB Griffin, JD Plummer
IEEE Transactions on Electron Devices 52 (1), 77-84, 2004
187 2004 Highly-scalable novel access device based on mixed ionic electronic conduction (MIEC) materials for high density phase change memory (PCM) arrays K Gopalakrishnan, RS Shenoy, CT Rettner, K Virwani, DS Bethune, ...
2010 Symposium on VLSI Technology, 205-206, 2010
155 2010 A scalable multi-TeraOPS deep learning processor core for AI trainina and inference B Fleischer, S Shukla, M Ziegler, J Silberman, J Oh, V Srinivasan, J Choi, ...
2018 IEEE symposium on VLSI circuits, 35-36, 2018
154 2018 Rectifying element for a crosspoint based memory array architecture K Gopalakrishnan
US Patent 8,203,873, 2012
138 2012 Rectifying element for a crosspoint based memory array architecture K Gopalakrishnan
US Patent 7,382,647, 2008
135 2008 Approximate computing: Challenges and opportunities A Agrawal, J Choi, K Gopalakrishnan, S Gupta, R Nair, J Oh, DA Prener, ...
2016 IEEE International Conference on Rebooting Computing (ICRC), 1-8, 2016
127 2016 