Srivatsan Krishnan
Srivatsan Krishnan
Verified email at seas.harvard.edu
Title
Cited by
Cited by
Year
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
3562017
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
1392016
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
642018
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
642018
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
492018
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
422018
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
24*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
122017
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
112020
Quantized reinforcement learning (quarl)
M Lam, S Chitlangia, S Krishnan, Z Wan, G Barth-Maron, A Faust, ...
arXiv preprint arXiv:1910.01055, 2019
112019
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, ...
arXiv preprint arXiv:1909.11236, 2019
102019
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
62020
Machine learning-based automated design space exploration for autonomous aerial robots
S Krishnan, Z Wan, K Bharadwaj, P Whatmough, A Faust, S Neuman, ...
arXiv preprint arXiv:2102.02988, 2021
42021
Why Compute Matters for UAV Energy Efficiency?
B Boroujerdian, H Genc, S Krishnan, W Ciu, A Faust, VJ Reddi
2nd International Symposium on Aerial Robotics, 2018
42018
Accelerator Templates and Runtime Support for Variable Precision CNN
S Krishnan, P Ratusziak, C Johnson, D Moss, S Subhaschandra
32017
The role of compute in autonomous aerial vehicles
B Boroujerdian, H Genc, S Krishnan, BP Duisterhof, B Plancher, ...
arXiv preprint arXiv:1906.10513, 2019
22019
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
12021
Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter
BP Duisterhof, S Krishnan, JJ Cruz, CR Banbury, W Fu, A Faust, ...
12021
Learning to seek: deep reinforcement learning for phototaxis of a nano drone in an obstacle field
BP Duisterhof, S Krishnan, JJ Cruz, CR Banbury, W Fu, A Faust, ...
arXiv e-prints, arXiv: 1909.11236, 2019
12019
Toward Exploring End-to-End Learning Algorithms for Autonomous Aerial Machines
S Krishnan, B Boroujerdian, A Faust, VJ Reddi
12019
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