| Can FPGAs beat GPUs in accelerating next-generation deep neural networks? E Nurvitadhi, G Venkatesh, J Sim, D Marr, R Huang, J Ong Gee Hock, ... Proceedings of the 2017 ACM/SIGDA international symposium on field …, 2017 | 617 | 2017 |
| Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC E Nurvitadhi, J Sim, D Sheffield, A Mishra, S Krishnan, D Marr 2016 26th International Conference on Field Programmable Logic and …, 2016 | 247 | 2016 |
| A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+ FPGA Platform: A Deep Learning Case Study DJM Moss, S Krishnan, E Nurvitadhi, P Ratuszniak, C Johnson, J Sim, ... Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018 | 105 | 2018 |
| MAVBench: Micro Aerial Vehicle Benchmarking B Borojerdian, H Genc, S Krishnan, W Cui, A Faust, V Janapareddi The 51st Annual IEEE/ACM International Symposium on Microarchitecture, 2018 | 100 | 2018 |
| Air learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation S Krishnan, B Boroujerdian, W Fu, A Faust, VJ Reddi Machine Learning 110 (9), 2501-2540, 2021 | 85* | 2021 |
| Widening access to applied machine learning with tinyml VJ Reddi, B Plancher, S Kennedy, L Moroney, P Warden, A Agarwal, ... arXiv preprint arXiv:2106.04008, 2021 | 73 | 2021 |
| Learning to seek: Autonomous source seeking with deep reinforcement learning onboard a nano drone microcontroller BP Duisterhof, S Krishnan, JJ Cruz, CR Banbury, W Fu, A Faust, ... 2021 IEEE International Conference on Robotics and Automation (ICRA), 2019 | 69* | 2019 |
| Hardware accelerator architecture and template for web-scale k-means clustering E Nurvitadhi, G Venkatesh, S Krishnan, S Subhaschandra, D Marr US Patent App. 15/396,515, 2018 | 68 | 2018 |
| QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning S Krishnan, S Chitlangia, M Lam, Z Wan, A Faust, VJ Reddi Transactions on Machine Learning Research 2022, 2022 | 51* | 2022 |
| Automatic Domain-Specific SoC Design for Autonomous Unmanned Aerial Vehicles S Krishnan, Z Wan, K Bharadwaj, P Whatmough, A Faust, S Neuman, ... 55th ACM/IEEE International Symposium on Microarchitecture (MICRO 2022), 2022 | 47* | 2022 |
| Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots SM Neuman, B Plancher, BP Duisterhof, S Krishnan, C Banbury, ... 2022 IEEE 4th International Conference on Artificial Intelligence Circuits …, 2022 | 42 | 2022 |
| The sky is not the limit: A visual performance model for cyber-physical co-design in autonomous machines S Krishnan, Z Wan, K Bhardwaj, P Whatmough, A Faust, GY Wei, ... IEEE Computer Architecture Letters 19 (1), 38-42, 2020 | 39 | 2020 |
| Roofline model for uavs: A bottleneck analysis tool for designing compute systems for autonomous drones S Krishnan, Z Wan, K Bhardwaj, A Faust, VJ Reddi arXiv preprint, 2021 | 27* | 2021 |
| Customizable FPGA OpenCL matrix multiply design template for deep neural networks J Yinger, E Nurvitadhi, D Capalija, A Ling, D Marr, S Krishnan, D Moss, ... Field Programmable Technology (ICFPT), 2017 International Conference on, 259-262, 2017 | 18 | 2017 |
| RL-Scope: Cross-stack profiling for deep reinforcement learning workloads J Gleeson, M Gabel, G Pekhimenko, E de Lara, S Krishnan, ... Proceedings of Machine Learning and Systems 3, 783-799, 2021 | 15 | 2021 |
| The role of compute in autonomous aerial vehicles B Boroujerdian, H Genc, S Krishnan, BP Duisterhof, B Plancher, ... ACM Transactions on Computer SystemsVolume 39 Issue 1-4, 2019 | 14* | 2019 |
| Methods and apparatus to provide user-level access authorization for cloud-based field-programmable gate arrays S Subhaschandra, S Krishnan, B Thomas, P Marolia US Patent 10,528,768, 2020 | 12 | 2020 |
| Archgym: An open-source gymnasium for machine learning assisted architecture design S Krishnan, A Yazdanbakhsh, S Prakash, J Jabbour, I Uchendu, S Ghosh, ... Proceedings of the 50th Annual International Symposium on Computer …, 2023 | 10 | 2023 |
| Autosoc: Automating algorithm-soc co-design for aerial robots S Krishnan, T Tambe, Z Wan, VJ Reddi arXiv preprint arXiv:2109.05683, 2021 | 10 | 2021 |
| Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration S Krishnan, N Jaques, S Omidshafiei, D Zhang, I Gur, VJ Reddi, A Faust arXiv preprint arXiv:2211.16385, 2022 | 6 | 2022 |
Vijay Janapa ReddiHarvard UniversityBestätigte E-Mail-Adresse bei eecs.harvard.edu
