The gem5 simulator N Binkert, B Beckmann, G Black, SK Reinhardt, A Saidi, A Basu, ... ACM SIGARCH Computer Architecture News 39 (2), 1-7, 2011 | 6119 | 2011 |
Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks YH Chen, T Krishna, JS Emer, V Sze IEEE journal of solid-state circuits 52 (1), 127-138, 2016 | 3864 | 2016 |
GARNET: A detailed on-chip network model inside a full-system simulator N Agarwal, T Krishna, LS Peh, NK Jha 2009 IEEE international symposium on performance analysis of systems and …, 2009 | 961 | 2009 |
Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable interconnects H Kwon, A Samajdar, T Krishna ACM SIGPLAN Notices 53 (2), 461-475, 2018 | 520 | 2018 |
Sigma: A sparse and irregular gemm accelerator with flexible interconnects for dnn training E Qin, A Samajdar, H Kwon, V Nadella, S Srinivasan, D Das, B Kaul, ... 2020 IEEE International Symposium on High Performance Computer Architecture …, 2020 | 444 | 2020 |
Understanding reuse, performance, and hardware cost of dnn dataflow: A data-centric approach H Kwon, P Chatarasi, M Pellauer, A Parashar, V Sarkar, T Krishna Proceedings of the 52nd Annual IEEE/ACM International Symposium on …, 2019 | 314 | 2019 |
Scale-sim: Systolic cnn accelerator simulator A Samajdar, Y Zhu, P Whatmough, M Mattina, T Krishna arXiv preprint arXiv:1811.02883, 2018 | 308 | 2018 |
The gem5 simulator: Version 20.0+ J Lowe-Power, AM Ahmad, A Akram, M Alian, R Amslinger, M Andreozzi, ... arXiv preprint arXiv:2007.03152, 2020 | 295 | 2020 |
A systematic methodology for characterizing scalability of dnn accelerators using scale-sim A Samajdar, JM Joseph, Y Zhu, P Whatmough, M Mattina, T Krishna 2020 IEEE International Symposium on Performance Analysis of Systems and …, 2020 | 185 | 2020 |
SCORPIO: A 36-core research chip demonstrating snoopy coherence on a scalable mesh NoC with in-network ordering BK Daya, CHO Chen, S Subramanian, WC Kwon, S Park, T Krishna, ... ACM SIGARCH Computer Architecture News 42 (3), 25-36, 2014 | 179 | 2014 |
Maestro: A data-centric approach to understand reuse, performance, and hardware cost of dnn mappings H Kwon, P Chatarasi, V Sarkar, T Krishna, M Pellauer, A Parashar IEEE micro 40 (3), 20-29, 2020 | 174 | 2020 |
On-chip networks NE Jerger, T Krishna, LS Peh Morgan & Claypool Publishers, 2017 | 161 | 2017 |
Breaking the on-chip latency barrier using SMART T Krishna, CHO Chen, WC Kwon, LS Peh 2013 IEEE 19th International Symposium on High Performance Computer …, 2013 | 152 | 2013 |
Characterizing the deployment of deep neural networks on commercial edge devices R Hadidi, J Cao, Y Xie, B Asgari, T Krishna, H Kim 2019 IEEE International Symposium on Workload Characterization (IISWC), 35-48, 2019 | 140 | 2019 |
Heterogeneous dataflow accelerators for multi-DNN workloads H Kwon, L Lai, M Pellauer, T Krishna, YH Chen, V Chandra 2021 IEEE International Symposium on High-Performance Computer Architecture …, 2021 | 136 | 2021 |
Co-exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks L Yang, Z Yan, M Li, H Kwon, L Lai, T Krishna, V Chandra, W Jiang, Y Shi 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 135 | 2020 |
Gamma: Automating the hw mapping of dnn models on accelerators via genetic algorithm SC Kao, T Krishna Proceedings of the 39th International Conference on Computer-Aided Design, 1-9, 2020 | 133 | 2020 |
SMART: A single-cycle reconfigurable NoC for SoC applications CHO Chen, S Park, T Krishna, S Subramanian, AP Chandrakasan, ... 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE), 338-343, 2013 | 131 | 2013 |
Towards the ideal on-chip fabric for 1-to-many and many-to-1 communication T Krishna, LS Peh, BM Beckmann, SK Reinhardt Proceedings of the 44th Annual IEEE/ACM International Symposium on …, 2011 | 124 | 2011 |
Approaching the theoretical limits of a mesh NoC with a 16-node chip prototype in 45nm SOI S Park, T Krishna, CH Chen, B Daya, A Chandrakasan, LS Peh Proceedings of the 49th Annual Design Automation Conference, 398-405, 2012 | 120 | 2012 |