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2020 – today
- 2023
[i34]Dhireesha Kudithipudi, Anurag Reddy Daram, Abdullah M. Zyarah, Fatima Tuz Zohora, James B. Aimone
, Angel Yanguas-Gil, Nicholas Soures, Emre Neftci, Matthew Mattina, Vincenzo Lomonaco, Clare D. Thiem, Benjamin R. Epstein:
Design Principles for Lifelong Learning AI Accelerators. CoRR abs/2310.04467 (2023)- 2022
- [c28]Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina:
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. HPCA 2022: 573-586 - [c27]Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough:
UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. NeurIPS 2022 - [i33]Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough:
UDC: Unified DNAS for Compressible TinyML Models. CoRR abs/2201.05842 (2022) - 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) - [c26]Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji:
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification. ICCVW 2021: 2095-2104 - [c25]Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Federated Learning Based on Dynamic Regularization. ICLR 2021 - [c24]Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Debiasing Model Updates for Improving Personalized Federated Training. ICML 2021: 21-31 - [c23]Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough:
Strong data processing inequality in neural networks with noisy neurons and its implications. ISIT 2021: 1170-1175 - [c22]Matthew Mattina:
Co-Designing Hardware and Models for Efficient On-Device ML Inference. ISLPED 2021: 1 - [c21]Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough:
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. MLSys 2021 - [c20]Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu:
Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices. MLSys 2021 - [c19]Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues:
On the effects of quantisation on model uncertainty in Bayesian neural networks. UAI 2021: 929-938 - [i32]Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough:
Information contraction in noisy binary neural networks and its implications. CoRR abs/2101.11750 (2021) - [i31]Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu:
Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices. CoRR abs/2102.07071 (2021) - [i30]Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues:
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks. CoRR abs/2102.11062 (2021) - [i29]Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina:
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. CoRR abs/2107.07983 (2021) - [i28]Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji:
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification. CoRR abs/2108.06317 (2021) - [i27]Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama:
Federated Learning Based on Dynamic Regularization. CoRR abs/2111.04263 (2021) - 2020
- [j2]Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina:
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. IEEE Comput. Archit. Lett. 19(1): 34-37 (2020) - [c18]Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina:
Ternary MobileNets via Per-Layer Hybrid Filter Banks. CVPR Workshops 2020: 3036-3046 - [c17]Zhi Gang Liu, Matthew Mattina:
Efficient Residue Number System Based Winograd Convolution. ECCV (19) 2020: 53-68 - [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 - [c15]Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu S. Talagala, David Hanwell, Matthew Mattina, Paul N. Whatmough:
ISP4ML: The Role of Image Signal Processing in Efficient Deep Learning Vision Systems. ICPR 2020: 2438-2445 - [c14]Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough:
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. INTERSPEECH 2020: 4054-4058 - [c13]Ananda Samajdar, Jan Moritz Joseph, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna:
A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim. ISPASS 2020: 58-68 - [c12]Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina:
Searching for Winograd-aware Quantized Networks. MLSys 2020 - [i26]Chuteng Zhou, Prad Kadambi, Matthew Mattina, Paul N. Whatmough:
Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation. CoRR abs/2001.04974 (2020) - [i25]Urmish Thakker, Paul N. Whatmough, Matthew Mattina, Jesse G. Beu:
Compressing Language Models using Doped Kronecker Products. CoRR abs/2001.08896 (2020) - [i24]Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina:
Searching for Winograd-aware Quantized Networks. CoRR abs/2002.10711 (2020) - [i23]Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina:
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. CoRR abs/2005.08098 (2020) - [i22]Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough:
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. CoRR abs/2005.11138 (2020) - [i21]Zhi Gang Liu, Matthew Mattina:
Efficient Residue Number System Based Winograd Convolution. CoRR abs/2007.12216 (2020) - [i20]Dibakar Gope, Jesse G. Beu, Matthew Mattina:
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. CoRR abs/2008.00638 (2020) - [i19]Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina:
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration. CoRR abs/2009.02381 (2020) - [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) - [i17]Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough:
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. CoRR abs/2010.11267 (2020)
2010 – 2019
- 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 - [c9]Zhi Gang Liu, Matthew Mattina:
Learning Low-precision Neural Networks without Straight-Through Estimator (STE). IJCAI 2019: 3066-3072 - [c8]Dibakar Gope, Ganesh Dasika, Matthew Mattina:
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. SysML 2019 - [c7]Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina:
FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer Learning. 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 - [c5]Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS 2019: 4978-4990 - [i16]Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina:
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning. CoRR abs/1902.11128 (2019) - [i15]Zhi Gang Liu, Matthew Mattina:
Learning low-precision neural networks without Straight-Through Estimator(STE). CoRR abs/1903.01061 (2019) - [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) - [i11]Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR abs/1905.12107 (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) - [i7]Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina:
Ternary MobileNets via Per-Layer Hybrid Filter Banks. CoRR abs/1911.01028 (2019) - [i6]Patrick Hansen, Alexey Vilkin, Yury Khrustalev, James Imber, David Hanwell, Matthew Mattina, Paul N. Whatmough:
ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems. CoRR abs/1911.07954 (2019) - 2018
- [c4]Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough:
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. ISCA 2018: 547-560 - [i5]Yuhao Zhu, Matthew Mattina, Paul N. Whatmough:
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective. CoRR abs/1801.06274 (2018) - [i4]Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough:
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. CoRR abs/1803.11232 (2018) - [i3]Ananda Samajdar, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna:
SCALE-Sim: Systolic CNN Accelerator. CoRR abs/1811.02883 (2018) - [i2]Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina:
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning. CoRR abs/1812.01672 (2018) - [i1]Franz Pernkopf, Wolfgang Roth, Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani:
Efficient and Robust Machine Learning for Real-World Systems. CoRR abs/1812.02240 (2018)
2000 – 2009
- 2008
- [c3]Shane Bell, Bruce Edwards, John Amann, Rich Conlin, Kevin Joyce, Vince Leung, John MacKay, Mike Reif, Liewei Bao, John F. Brown III, Matthew Mattina, Chyi-Chang Miao, Carl Ramey, David Wentzlaff, Walker Anderson, Ethan Berger, Nat Fairbanks, Durlov Khan, Froilan Montenegro, Jay Stickney, John Zook:
TILE64 - Processor: A 64-Core SoC with Mesh Interconnect. ISSCC 2008: 88-89 - 2007
- [j1]David Wentzlaff, Patrick Griffin, Henry Hoffmann, Liewei Bao, Bruce Edwards, Carl Ramey, Matthew Mattina, Chyi-Chang Miao, John F. Brown III, Anant Agarwal:
On-Chip Interconnection Architecture of the Tile Processor. IEEE Micro 27(5): 15-31 (2007) - 2006
- [c2]Aamer Jaleel, Matthew Mattina, Bruce L. Jacob:
Last level cache (LLC) performance of data mining workloads on a CMP - a case study of parallel bioinformatics workloads. HPCA 2006: 88-98 - 2002
- [c1]Roger Espasa, Federico Ardanaz, Julio Gago, Roger Gramunt, Isaac Hernandez, Toni Juan, Joel S. Emer, Stephen Felix, P. Geoffrey Lowney, Matthew Mattina, André Seznec:
Tarantula: A Vector Extension to the Alpha Architecture. ISCA 2002: 281-292
Coauthor Index
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