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ORCIDMatthew Mattina
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Publications
- 2021
[j3]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
- [c16]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 - [i18]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) - 2019
- [c11]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 - [c10]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 - [c8]Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. SysML 2019 - [c6]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 - [i14]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) - [i13]Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. CoRR abs/1903.01531 (2019) - [i12]Urmish Thakker
, Ganesh Dasika, Jesse G. Beu, Matthew Mattina:
Measuring scheduling efficiency of RNNs for NLP applications. CoRR abs/1904.03302 (2019) - [i10]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) - [i9]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) - [i8]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)
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