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MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

Oct 25, 2020
Colby Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough

* 10 pages, 8 figures, 3 tables, appendix 

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Rank and run-time aware compression of NLP Applications

Oct 06, 2020
Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina

* Published at SustaiNLP@EMNLP 2020. arXiv admin note: text overlap with arXiv:1906.04886 

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Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration

Sep 04, 2020
Zhi-Gang Liu, Paul N. Whatmough, Matthew Mattina


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High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands

Aug 03, 2020
Dibakar Gope, Jesse Beu, Matthew Mattina


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Efficient Residue Number System Based Winograd Convolution

Jul 23, 2020
Zhi-Gang Liu, Matthew Mattina

* Accepted by ECCV2020 Conference 

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TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids

May 20, 2020
Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough

* First four authors contributed equally. For audio samples, see https://github.com/BoseCorp/efficient-neural-speech-enhancement 

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Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference

May 16, 2020
Zhi-Gang Liu, Paul N. Whatmough, Matthew Mattina

* Accepted by IEEE Computer Architecture Letters on 3/4/2020 

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Searching for Winograd-aware Quantized Networks

Feb 25, 2020
Javier Fernandez-Marques, Paul N. Whatmough, Andrew Mundy, Matthew Mattina

* Published as a conference paper at MLSys 2020 

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Compressing Language Models using Doped Kronecker Products

Jan 31, 2020
Urmish Thakker, Paul Whatamough, Matthew Mattina, Jesse Beu

* Presented at On-device Intelligence Workshop at Third Conference on Machine Learning and Systems (MLSys) 2020 
* Link to Workshop - https://mlsys.org/Conferences/2020/Schedule?showEvent=1297 

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Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation

Jan 14, 2020
Chuteng Zhou, Prad Kadambi, Matthew Mattina, Paul N. Whatmough


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ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems

Nov 25, 2019
Patrick Hansen, Alexey Vilkin, Yury Khrustalev, James Imber, David Hanwell, Matthew Mattina, Paul N. Whatmough

* 13 pages, 11 figures 

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Ternary MobileNets via Per-Layer Hybrid Filter Banks

Nov 04, 2019
Dibakar Gope, Jesse Beu, Urmish Thakker, Matthew Mattina


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Pushing the limits of RNN Compression

Oct 09, 2019
Urmish Thakker, Igor Fedorov, Jesse Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina

* 5th edition of Workshop on Energy Efficient Machine Learning and Cognitive Computing at NeurIPS 2019 
* 6 pages. arXiv admin note: substantial text overlap with arXiv:1906.02876 

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Compressing RNNs for IoT devices by 15-38x using Kronecker Products

Jun 18, 2019
Urmish Thakker, Jesse Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina


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Run-Time Efficient RNN Compression for Inference on Edge Devices

Jun 18, 2019
Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina

* Published at 4th edition of Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications at International Symposium of Computer Architecture 2019, Phoenix, Arizona (https://www.emc2-workshop.com/isca-19) colocated with ISCA 2019 

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SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

May 28, 2019
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough


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Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications

Mar 04, 2019
Dibakar Gope, Ganesh Dasika, Matthew Mattina

* 2nd Conference on Systems and Machine Learning (SysML), 2019 

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Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs

Mar 04, 2019
Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert Mullins

* HPCA.EMC2 Feb 17, 2019 

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Learning low-precision neural networks without Straight-Through Estimator(STE)

Mar 04, 2019
Zhi-Gang Liu, Matthew Mattina


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FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning

Feb 27, 2019
Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas Kolala Venkataramanaiah, Jae-sun Seo, Matthew Mattina

* 10 pages, 8 figures, paper accepted at SysML2019 conference 

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Efficient and Robust Machine Learning for Real-World Systems

Dec 05, 2018
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Guenther Schindler, Holger Froening, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani


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Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning

Dec 04, 2018
Paul Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina

* 4 pages, 2 figures, NeurIPS 2018 on-device ML workshop 

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Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision

Mar 29, 2018
Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul Whatmough


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Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective

Feb 01, 2018
Yuhao Zhu, Matthew Mattina, Paul Whatmough


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