| Pin: building customized program analysis tools with dynamic instrumentation CK Luk, R Cohn, R Muth, H Patil, A Klauser, G Lowney, S Wallace, ... Acm sigplan notices 40 (6), 190-200, 2005 | 5754 | 2005 |
| GPUWattch: Enabling energy optimizations in GPGPUs J Leng, T Hetherington, A ElTantawy, S Gilani, NS Kim, TM Aamodt, ... ACM SIGARCH Computer Architecture News 41 (3), 487-498, 2013 | 780 | 2013 |
| Tensorflow lite micro: Embedded machine learning for tinyml systems R David, J Duke, A Jain, V Janapa Reddi, N Jeffries, J Li, N Kreeger, ... Proceedings of Machine Learning and Systems 3, 800-811, 2021 | 644 | 2021 |
| Mlperf inference benchmark VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 555 | 2020 |
| Deep reinforcement learning for cyber security TT Nguyen, VJ Reddi IEEE Transactions on Neural Networks and Learning Systems, 2019 | 525 | 2019 |
| Mlperf training benchmark P Mattson, C Cheng, G Diamos, C Coleman, P Micikevicius, D Patterson, ... Proceedings of Machine Learning and Systems 2, 336-349, 2020 | 345 | 2020 |
| A dynamic compilation framework for controlling microprocessor energy and performance Q Wu, VJ Reddi, Y Wu, J Lee, D Connors, D Brooks, M Martonosi, ... 38th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO'05 …, 2005 | 293 | 2005 |
| Benchmarking tinyml systems: Challenges and direction CR Banbury, VJ Reddi, M Lam, W Fu, A Fazel, J Holleman, X Huang, ... arXiv preprint arXiv:2003.04821, 2020 | 292 | 2020 |
| Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers C Banbury, C Zhou, I Fedorov, R Matas, U Thakker, D Gope, ... Proceedings of machine learning and systems 3, 517-532, 2021 | 276 | 2021 |
| Web search using mobile cores: quantifying and mitigating the price of efficiency V Janapa Reddi, BC Lee, T Chilimbi, K Vaid ACM SIGARCH Computer Architecture News 38 (3), 314-325, 2010 | 260 | 2010 |
| Pin: a binary instrumentation tool for computer architecture research and education VJ Reddi, A Settle, DA Connors, RS Cohn Proceedings of the 2004 workshop on Computer architecture education: held in …, 2004 | 241 | 2004 |
| PLR: A software approach to transient fault tolerance for multicore architectures A Shye, J Blomstedt, T Moseley, VJ Reddi, DA Connors IEEE Transactions on Dependable and Secure Computing 6 (2), 135-148, 2008 | 218 | 2008 |
| High-performance and energy-efficient mobile web browsing on big/little systems Y Zhu, VJ Reddi 2013 IEEE 19th International Symposium on High Performance Computer …, 2013 | 216 | 2013 |
| MLPerf tiny benchmark B Colby, RV Janapa, T Peter, J Nat, K Csaba, M Pietro, K David, ... Advances in Neural Information Processing Systems 34 (1), 15, 2021 | 212* | 2021 |
| Mobile CPU's rise to power: Quantifying the impact of generational mobile CPU design trends on performance, energy, and user satisfaction M Halpern, Y Zhu, VJ Reddi 2016 IEEE International Symposium on High Performance Computer Architecture …, 2016 | 202 | 2016 |
| MLPerf: An industry standard benchmark suite for machine learning performance P Mattson, VJ Reddi, C Cheng, C Coleman, G Diamos, D Kanter, ... IEEE Micro 40 (2), 8-16, 2020 | 183 | 2020 |
| Using process-level redundancy to exploit multiple cores for transient fault tolerance A Shye, T Moseley, VJ Reddi, J Blomstedt, DA Connors 37th Annual IEEE/IFIP International Conference on Dependable Systems and …, 2007 | 164 | 2007 |
| Voltage emergency prediction: Using signatures to reduce operating margins VJ Reddi, MS Gupta, G Holloway, GY Wei, MD Smith, D Brooks 2009 IEEE 15th International Symposium on High Performance Computer …, 2009 | 149 | 2009 |
| Event-based scheduling for energy-efficient qos (eqos) in mobile web applications Y Zhu, M Halpern, VJ Reddi 2015 IEEE 21st International Symposium on High Performance Computer …, 2015 | 132 | 2015 |
| Dataperf: Benchmarks for data-centric ai development M Mazumder, C Banbury, X Yao, B Karlaš, W Gaviria Rojas, S Diamos, ... Advances in Neural Information Processing Systems 36, 2024 | 131 | 2024 |
David BrooksHaley Family Professor of Computer Science, Harvard UniversityBestätigte E-Mail-Adresse bei eecs.harvard.edu
