| Die stacking (3D) microarchitecture B Black, M Annavaram, N Brekelbaum, J DeVale, L Jiang, GH Loh, ... 2006 39th Annual IEEE/ACM International Symposium on Microarchitecture …, 2006 | 805 | 2006 |
| A framework of energy efficient mobile sensing for automatic user state recognition Y Wang, J Lin, M Annavaram, QA Jacobson, J Hong, B Krishnamachari, ... Proceedings of the 7th international conference on Mobile systems …, 2009 | 632 | 2009 |
| Virtual trip lines for distributed privacy-preserving traffic monitoring B Hoh, M Gruteser, R Herring, J Ban, D Work, JC Herrera, AM Bayen, ... Proceedings of the 6th international conference on Mobile systems …, 2008 | 501 | 2008 |
| Fedml: A research library and benchmark for federated machine learning C He, S Li, J So, X Zeng, M Zhang, H Wang, X Wang, P Vepakomma, ... arXiv preprint arXiv:2007.13518, 2020 | 471 | 2020 |
| Group knowledge transfer: Federated learning of large cnns at the edge C He, M Annavaram, S Avestimehr Advances in Neural Information Processing Systems 33, 14068-14080, 2020 | 453 | 2020 |
| Mitigating Amdahl's law through EPI throttling M Annavaram, E Grochowski, J Shen 32nd International Symposium on Computer Architecture (ISCA'05), 298-309, 2005 | 324 | 2005 |
| Data prefetching by dependence graph precomputation M Annavaram, JM Patel, ES Davidson ACM SIGARCH Computer Architecture News 29 (2), 52-61, 2001 | 247 | 2001 |
| Method and apparatus for varying energy per instruction according to the amount of available parallelism E Grochowski, J Shen, H Wang, D Orenstein, GS Sheaffer, R Ronen, ... US Patent 7,437,581, 2008 | 201 | 2008 |
| Fedgraphnn: A federated learning system and benchmark for graph neural networks C He, K Balasubramanian, E Ceyani, C Yang, H Xie, L Sun, L He, L Yang, ... arXiv preprint arXiv:2104.07145, 2021 | 191 | 2021 |
| Summarizer: trading communication with computing near storage G Koo, KK Matam, T I, HVKG Narra, J Li, HW Tseng, S Swanson, ... Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 169 | 2017 |
| Warped register file: A power efficient register file for GPGPUs M Abdel-Majeed, M Annavaram 2013 IEEE 19th International symposium on high performance computer …, 2013 | 165 | 2013 |
| Multimodal physical activity recognition by fusing temporal and cepstral information M Li, V Rozgić, G Thatte, S Lee, A Emken, M Annavaram, U Mitra, ... IEEE Transactions on Neural Systems and Rehabilitation Engineering 18 (4 …, 2010 | 161 | 2010 |
| Warped-compression: Enabling power efficient GPUs through register compression S Lee, K Kim, G Koo, H Jeon, WW Ro, M Annavaram ACM SIGARCH Computer Architecture News 43 (3S), 502-514, 2015 | 149 | 2015 |
| Knightshift: Scaling the energy proportionality wall through server-level heterogeneity D Wong, M Annavaram 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture, 119-130, 2012 | 149 | 2012 |
| Energy per instruction trends in Intel microprocessors E Grochowski, M Annavaram Technology@ Intel Magazine 4 (3), 1-8, 2006 | 147 | 2006 |
| Warped-slicer: Efficient intra-SM slicing through dynamic resource partitioning for GPU multiprogramming Q Xu, H Jeon, K Kim, WW Ro, M Annavaram ACM SIGARCH Computer Architecture News 44 (3), 230-242, 2016 | 140 | 2016 |
| GPU register file virtualization H Jeon, GS Ravi, NS Kim, M Annavaram Proceedings of the 48th International Symposium on Microarchitecture, 420-432, 2015 | 125 | 2015 |
| Towards non-IID and invisible data with FedNAS: Federated deep learning via neural architecture search C He, M Annavaram, S Avestimehr arXiv preprint arXiv:2004.08546, 2020 | 123 | 2020 |
| Parallel computer organization and design M Dubois, M Annavaram, P Stenström cambridge university press, 2012 | 120 | 2012 |
| Graph processing on GPUs: Where are the bottlenecks? Q Xu, H Jeon, M Annavaram 2014 IEEE International Symposium on Workload Characterization (IISWC), 140-149, 2014 | 119 | 2014 |
