Matthew Mattina

Publication Activity (10 Years)

Years Active: 2002-2023
Publications (10 Years): 61

Top Topics

Kronecker Product

Transfer Learning

Signal Processing

Top Venues

EMC2@HPCA/CVPR/ISCA

Publications

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 (2023)
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)
Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. HPCA (2022)
Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough
UDC: Unified DNAS for Compressible TinyML Models. CoRR (2022)
Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough
Strong data processing inequality in neural networks with noisy neurons and its implications. ISIT (2021)
Matthew Mattina
Co-Designing Hardware and Models for Efficient On-Device ML Inference. ISLPED (2021)
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)
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)
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 (2021)
Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification. ICCVW (2021)
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)
Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. CoRR (2021)
Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough
Information contraction in noisy binary neural networks and its implications. CoRR (2021)
Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification. CoRR (2021)
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) (2021)
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama
Federated Learning Based on Dynamic Regularization. CoRR (2021)
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama
Federated Learning Based on Dynamic Regularization. ICLR (2021)
Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks. CoRR (2021)
Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues
On the effects of quantisation on model uncertainty in Bayesian neural networks. UAI (2021)
Dibakar Gope, Jesse G. Beu, Matthew Mattina
High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. CoRR (2020)
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 (2020)
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)
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) (2020)
Urmish Thakker, Paul N. Whatmough, Matthew Mattina, Jesse G. Beu
Compressing Language Models using Doped Kronecker Products. CoRR (2020)
Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
Rank and run-time aware compression of NLP Applications. CoRR (2020)
Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina
Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. CoRR (2020)
Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina
Searching for Winograd-aware Quantized Networks. CoRR (2020)
Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina
Searching for Winograd-aware Quantized Networks. MLSys (2020)
Zhi Gang Liu, Matthew Mattina
Efficient Residue Number System Based Winograd Convolution. ECCV (19) (2020)
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 (2020)
Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
Rank and run-time aware compression of NLP Applications. SustaiNLP@EMNLP (2020)
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)
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 (2020)
Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina
Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration. CoRR (2020)
Zhi Gang Liu, Matthew Mattina
Efficient Residue Number System Based Winograd Convolution. CoRR (2020)
Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu Talagala, David Hanwell, Matthew Mattina, Paul N. Whatmough
ISP4ML: The Role of Image Signal Processing in Efficient Deep Learning Vision Systems. ICPR (2020)
Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina
Ternary MobileNets via Per-Layer Hybrid Filter Banks. CVPR Workshops (2020)
Dibakar Gope, Ganesh Dasika, Matthew Mattina
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. CoRR (2019)
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 (2019)
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 (2019)
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS (2019)
Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina
Ternary MobileNets via Per-Layer Hybrid Filter Banks. CoRR (2019)
Zhi Gang Liu, Matthew Mattina
Learning low-precision neural networks without Straight-Through Estimator(STE). CoRR (2019)
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)
Dibakar Gope, Ganesh Dasika, Matthew Mattina
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. MLSys (2019)
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR (2019)
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. MLSys (2019)
Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
Pushing the limits of RNN Compression. CoRR (2019)
Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
Pushing the limits of RNN Compression. EMC2@NeurIPS (2019)
Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
Run-Time Efficient RNN Compression for Inference on Edge Devices. CoRR (2019)
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)
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 (2019)
Urmish Thakker, Ganesh Dasika, Jesse G. Beu, Matthew Mattina
Measuring scheduling efficiency of RNNs for NLP applications. CoRR (2019)
Zhi Gang Liu, Matthew Mattina
Learning Low-precision Neural Networks without Straight-Through Estimator (STE). IJCAI (2019)
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 (2019)
Ananda Samajdar, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna
SCALE-Sim: Systolic CNN Accelerator. CoRR (2018)
Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning. CoRR (2018)
Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. ISCA (2018)
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 (2018)
Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. CoRR (2018)
Yuhao Zhu, Matthew Mattina, Paul N. Whatmough
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective. CoRR (2018)
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)
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) (2007)
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)
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)