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
3302017
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
1212016
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
592018
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
592018
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
412018
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
332018
Air learning: An AI research platform for algorithm-hardware benchmarking of autonomous aerial robots
S Krishnan, B Borojerdian, W Fu, A Faust, VJ Reddi
arXiv preprint arXiv:1906.00421, 2019
222019
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
92020
Quantized Reinforcement Learning (QUARL)
S Krishnan, S Chitlangia, M Lam, Z Wan, A Faust, VJ Reddi
arXiv preprint arXiv:1910.01055, 2019
82019
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
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
42020
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
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
32019
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
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 preprint arXiv:1909.11236, 2019
12019
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
J Gleeson, S Krishnan, M Gabel, VJ Reddi, E de Lara, G Pekhimenko
arXiv preprint arXiv:2102.04285, 2021
2021
Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter
BP Duisterhof, S Krishnan, JJ Cruz, CR Banbury, W Fu, A Faust, ...
2021
Learning to Seek: Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter
BP Duisterhof, S Krishnan, JJ Cruz, CR Banbury, W Fu, A Faust, ...
2020
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