Matthew Mattina

65 papers A* 9A 3B 3Journal 37Unranked 13

Year	Rank	Type	Title / Venue / Authors
2023	J	`jnl`	Design Principles for Lifelong Learning AI Accelerators. CoRR 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
2022	A*	`conf`	S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. HPCA Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina
2022	A*	`conf`	UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units. NeurIPS Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough
2022	J	`jnl`	UDC: Unified DNAS for Compressible TinyML Models. CoRR Igor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough
2021	A	`conf`	Co-Designing Hardware and Models for Efficient On-Device ML Inference. ISLPED Matthew Mattina
2021	J	`jnl`	Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products. ACM J. Emerg. Technol. Comput. Syst. Urmish Thakker, Igor Fedorov, Chu Zhou, Dibakar Gope, Matthew Mattina, Ganesh Dasika, Jesse G. Beu
2021	A*	`conf`	Debiasing Model Updates for Improving Personalized Federated Training. ICML Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama
2021	—	`conf`	Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices. MLSys Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu
2021	J	`jnl`	Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices. CoRR Urmish Thakker, Paul N. Whatmough, Zhi Gang Liu, Matthew Mattina, Jesse G. Beu
2021	A*	`conf`	Federated Learning Based on Dynamic Regularization. ICLR Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama
2021	J	`jnl`	Federated Learning Based on Dynamic Regularization. CoRR Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama
2021	J	`jnl`	Information contraction in noisy binary neural networks and its implications. CoRR Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough
2021	—	`conf`	MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. MLSys Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough
2021	J	`jnl`	On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks. CoRR Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues
2021	A	`conf`	On the effects of quantisation on model uncertainty in Bayesian neural networks. UAI Martin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues
2021	J	`jnl`	S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration. CoRR Zhi Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina
2021	B	`conf`	Strong data processing inequality in neural networks with noisy neurons and its implications. ISIT Chuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough
2021	—	`conf`	Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification. ICCVW Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji
2021	J	`jnl`	Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification. CoRR Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji
2020	B	`conf`	A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim. ISPASS Ananda Samajdar, Jan Moritz Joseph, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna
2020	J	`jnl`	Compressing Language Models using Doped Kronecker Products. CoRR Urmish Thakker, Paul N. Whatmough, Matthew Mattina, Jesse G. Beu
2020	—	`conf`	Efficient Residue Number System Based Winograd Convolution. ECCV (19) Zhi Gang Liu, Matthew Mattina
2020	J	`jnl`	Efficient Residue Number System Based Winograd Convolution. CoRR Zhi Gang Liu, Matthew Mattina
2020	J	`jnl`	High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. CoRR Dibakar Gope, Jesse G. Beu, Matthew Mattina
2020	B	`conf`	ISP4ML: The Role of Image Signal Processing in Efficient Deep Learning Vision Systems. ICPR Patrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu S. Talagala, David Hanwell, Matthew Mattina, Paul N. Whatmough
2020	J	`jnl`	MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers. CoRR Colby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough
2020	J	`jnl`	Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation. CoRR Chuteng Zhou, Prad Kadambi, Matthew Mattina, Paul N. Whatmough
2020	—	`conf`	Rank and run-time aware compression of NLP Applications. SustaiNLP@EMNLP Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
2020	J	`jnl`	Rank and run-time aware compression of NLP Applications. CoRR Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
2020	—	`conf`	Searching for Winograd-aware Quantized Networks. MLSys Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina
2020	J	`jnl`	Searching for Winograd-aware Quantized Networks. CoRR Javier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina
2020	J	`jnl`	Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration. CoRR Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina
2020	J	`jnl`	Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. IEEE Comput. Archit. Lett. Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina
2020	J	`jnl`	Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference. CoRR Zhi Gang Liu, Paul N. Whatmough, Matthew Mattina
2020	—	`conf`	Ternary MobileNets via Per-Layer Hybrid Filter Banks. CVPR Workshops Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina
2020	A	`conf`	TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. INTERSPEECH Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough
2020	J	`jnl`	TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids. CoRR Igor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough
2019	J	`jnl`	Compressing RNNs for IoT devices by 15-38x using Kronecker Products. CoRR Urmish Thakker, Jesse G. Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, Matthew Mattina
2019	—	`conf`	Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs. EMC2@HPCA/CVPR/ISCA Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse G. Beu, Matthew Mattina, Robert Mullins
2019	J	`jnl`	Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs. CoRR Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse G. Beu, Matthew Mattina, Robert Mullins
2019	J	`jnl`	FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning. CoRR Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina
2019	—	`conf`	FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer Learning. SysML Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina
2019	J	`jnl`	ISP4ML: Understanding the Role of Image Signal Processing in Efficient Deep Learning Vision Systems. CoRR Patrick Hansen, Alexey Vilkin, Yury Khrustalev, James Imber, David Hanwell, Matthew Mattina, Paul N. Whatmough
2019	A*	`conf`	Learning Low-precision Neural Networks without Straight-Through Estimator (STE). IJCAI Zhi Gang Liu, Matthew Mattina
2019	J	`jnl`	Learning low-precision neural networks without Straight-Through Estimator(STE). CoRR Zhi Gang Liu, Matthew Mattina
2019	J	`jnl`	Measuring scheduling efficiency of RNNs for NLP applications. CoRR Urmish Thakker, Ganesh Dasika, Jesse G. Beu, Matthew Mattina
2019	—	`conf`	Pushing the limits of RNN Compression. EMC2@NeurIPS Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
2019	J	`jnl`	Pushing the limits of RNN Compression. CoRR Urmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina
2019	—	`conf`	Run-Time Efficient RNN Compression for Inference on Edge Devices. EMC2@HPCA/CVPR/ISCA Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
2019	J	`jnl`	Run-Time Efficient RNN Compression for Inference on Edge Devices. CoRR Urmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
2019	A*	`conf`	SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
2019	J	`jnl`	SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
2019	—	`conf`	Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. SysML Dibakar Gope, Ganesh Dasika, Matthew Mattina
2019	J	`jnl`	Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. CoRR Dibakar Gope, Ganesh Dasika, Matthew Mattina
2019	J	`jnl`	Ternary MobileNets via Per-Layer Hybrid Filter Banks. CoRR Dibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina
2018	J	`jnl`	Efficient and Robust Machine Learning for Real-World Systems. CoRR Franz Pernkopf, Wolfgang Roth, Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani
2018	J	`jnl`	Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning. CoRR Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina
2018	A*	`conf`	Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. ISCA Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough
2018	J	`jnl`	Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision. CoRR Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough
2018	J	`jnl`	Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective. CoRR Yuhao Zhu, Matthew Mattina, Paul N. Whatmough
2018	J	`jnl`	SCALE-Sim: Systolic CNN Accelerator. CoRR Ananda Samajdar, Yuhao Zhu, Paul N. Whatmough, Matthew Mattina, Tushar Krishna
2008	—	`conf`	TILE64 - Processor: A 64-Core SoC with Mesh Interconnect. ISSCC 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
2007	J	`jnl`	On-Chip Interconnection Architecture of the Tile Processor. IEEE Micro David Wentzlaff, Patrick Griffin, Henry Hoffmann, Liewei Bao, Bruce Edwards, Carl Ramey, Matthew Mattina, Chyi-Chang Miao, John F. Brown III, Anant Agarwal
2006	A*	`conf`	Last level cache (LLC) performance of data mining workloads on a CMP - a case study of parallel bioinformatics workloads. HPCA Aamer Jaleel, Matthew Mattina, Bruce L. Jacob
2002	A*	`conf`	Tarantula: A Vector Extension to the Alpha Architecture. ISCA Roger Espasa, Federico Ardanaz, Julio Gago, Roger Gramunt, Isaac Hernandez, Toni Juan, Joel S. Emer, Stephen Felix, P. Geoffrey Lowney, Matthew Mattina, André Seznec