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2020 – today
- 2021
[j2]Urmish Thakker, Igor Fedorov, Chu Zhou, Dibakar Gope, Matthew Mattina, Ganesh Dasika, Jesse G. Beu:
Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products. ACM J. Emerg. Technol. Comput. Syst. 17(4): 46:1-46:18 (2021)- 2020
- [c9]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Rank and run-time aware compression of NLP Applications. SustaiNLP@EMNLP 2020: 8-18 - [i7]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Rank and run-time aware compression of NLP Applications. CoRR abs/2010.03193 (2020)
2010 – 2019
- 2019
- [c8]Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse G. Beu, Matthew Mattina, Robert D. Mullins:
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs. EMC2@HPCA/CVPR/ISCA 2019: 1-5 - [c7]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Run-Time Efficient RNN Compression for Inference on Edge Devices. EMC2@HPCA/CVPR/ISCA 2019: 26-30 - [c6]Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. SysML 2019 - [c5]Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina:
Pushing the limits of RNN Compression. EMC2@NeurIPS 2019: 18-21 - [c4]Jin Tao, Urmish Thakker
, Ganesh Dasika, Jesse G. Beu:
Skipping RNN State Updates without Retraining the Original Model. SenSys-ML 2019: 31-36 - [i6]Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse G. Beu, Matthew Mattina, Robert D. Mullins:
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs. CoRR abs/1903.01521 (2019) - [i5]Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. CoRR abs/1903.01531 (2019) - [i4]Urmish Thakker, Ganesh Dasika, Jesse G. Beu, Matthew Mattina:
Measuring scheduling efficiency of RNNs for NLP applications. CoRR abs/1904.03302 (2019) - [i3]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina:
Compressing RNNs for IoT devices by 15-38x using Kronecker Products. CoRR abs/1906.02876 (2019) - [i2]Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Run-Time Efficient RNN Compression for Inference on Edge Devices. CoRR abs/1906.04886 (2019) - [i1]Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina:
Pushing the limits of RNN Compression. CoRR abs/1910.02558 (2019) - 2018
- [j1]Yu Cao, Xin Li, Jae-sun Seo, Ganesh Dasika:
Guest Editors' Introduction: Frontiers of Hardware and Algorithms for On-chip Learning. ACM J. Emerg. Technol. Comput. Syst. 14(2): 14:1-14:2 (2018) - 2017
- [c3]Tim Llewellynn, Maria del Milagro Fernández-Carrobles, Oscar Déniz, Samuel Fricker, Amos J. Storkey, Nuria Pazos, Gordana Velikic, Kirsten Leufgen, Rozenn Dahyot, Sebastian Koller, Georgios I. Goumas, Peter Leitner, Ganesh Dasika, Lei Wang, Kurt Tutschku:
BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited paper. Conf. Computing Frontiers 2017: 299-304 - 2016
- [c2]Naveen Suda, Vikas Chandra, Ganesh Dasika, Abinash Mohanty, Yufei Ma, Sarma B. K. Vrudhula, Jae-sun Seo, Yu Cao:
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks. FPGA 2016: 16-25 - [c1]Yordan P. Raykov, Emre Ozer, Ganesh Dasika, Alexis Boukouvalas, Max A. Little:
Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction. UbiComp 2016: 1016-1027
Coauthor Index
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