Matthew Mattina - researchr alias

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Viewing Publication 1 - 31 from 31

2022

UDC: Unified DNAS for Compressible TinyML Models for Neural Processing UnitsIgor Fedorov, Ramon Matas Navarro, Hokchhay Tann, Chuteng Zhou, Matthew Mattina, Paul N. Whatmough. nips 2022: [doi]

S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN AccelerationZhi-gang Liu, Paul N. Whatmough, Yuhao Zhu 0001, Matthew Mattina. hpca 2022: 573-586 [doi]

2021

Federated Learning Based on Dynamic RegularizationDurmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama. iclr 2021: [doi]

Debiasing Model Updates for Improving Personalized Federated TrainingDurmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama. icml 2021: 21-31 [doi]

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity MicrocontrollersColby R. Banbury, Chuteng Zhou, Igor Fedorov, Ramon Matas Navarro, Urmish Thakker, Dibakar Gope, Vijay Janapa Reddi, Matthew Mattina, Paul N. Whatmough. mlsys 2021: [doi]

On the effects of quantisation on model uncertainty in Bayesian neural networksMartin Ferianc, Partha Maji, Matthew Mattina, Miguel Rodrigues. uai 2021: 929-938 [doi]

Co-Designing Hardware and Models for Efficient On-Device ML InferenceMatthew Mattina. islped 2021: 1 [doi]

Towards Efficient Point Cloud Graph Neural Networks Through Architectural SimplificationShyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji. iccvw 2021: 2095-2104 [doi]

Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive MatricesUrmish Thakker, Paul N. Whatmough, Zhi-gang Liu, Matthew Mattina, Jesse G. Beu. mlsys 2021: [doi]

Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker ProductsUrmish Thakker, Igor Fedorov, Chu Zhou, Dibakar Gope, Matthew Mattina, Ganesh Dasika, Jesse G. Beu. jetc, 17(4), 2021. [doi]

Strong data processing inequality in neural networks with noisy neurons and its implicationsChuteng Zhou, Quntao Zhuang, Matthew Mattina, Paul N. Whatmough. isit 2021: 1170-1175 [doi]

2020

TinyLSTMs: Efficient Neural Speech Enhancement for Hearing AidsIgor Fedorov, Marko Stamenovic, Carl Jensen, Li-Chia Yang, Ari Mandell, Yiming Gan, Matthew Mattina, Paul N. Whatmough. interspeech 2020: 4054-4058 [doi]

Searching for Winograd-aware Quantized NetworksJavier Fernández-Marqués, Paul N. Whatmough, Andrew Mundy, Matthew Mattina. mlsys 2020: [doi]

Ternary MobileNets via Per-Layer Hybrid Filter BanksDibakar Gope, Jesse G. Beu, Urmish Thakker, Matthew Mattina. cvpr 2020: 3036-3046 [doi]

ISP4ML: The Role of Image Signal Processing in Efficient Deep Learning Vision SystemsPatrick Hansen, Alexey Vilkin, Yury Krustalev, James Imber, Dumidu Talagala, David Hanwell, Matthew Mattina, Paul N. Whatmough. icpr 2021: 2438-2445 [doi]

Efficient Residue Number System Based Winograd ConvolutionZhi-gang Liu, Matthew Mattina. eccv 2020: 53-68 [doi]

Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN InferenceZhi-gang Liu, Paul N. Whatmough, Matthew Mattina. cal, 19(1):34-37, 2020. [doi]

A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-SimAnanda Samajdar, Jan Moritz Joseph, Yuhao Zhu 0001, Paul N. Whatmough, Matthew Mattina, Tushar Krishna. ispass 2020: 58-68 [doi]

Rank and run-time aware compression of NLP ApplicationsUrmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina. emnlp 2020: 8-18 [doi]

2019

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained MicrocontrollersIgor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough. nips 2019: 4978-4990 [doi]

Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT ApplicationsDibakar Gope, Ganesh Dasika, Matthew Mattina. mlsys 2019: [doi]

Learning Low-precision Neural Networks without Straight-Through Estimator (STE)Zhi-gang Liu, Matthew Mattina. IJCAI 2019: 3066-3072 [doi]

Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUsPartha Maji, Andrew Mundy, Ganesh Dasika, Jesse G. Beu, Matthew Mattina, Robert D. Mullins. emc2 2019: 1-5 [doi]

Run-Time Efficient RNN Compression for Inference on Edge DevicesUrmish Thakker, Jesse G. Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina. emc2 2019: 26-30 [doi]

Pushing the limits of RNN CompressionUrmish Thakker, Igor Fedorov, Jesse G. Beu, Dibakar Gope, Chu Zhou, Ganesh Dasika, Matthew Mattina. nips 2019: 18-21 [doi]

FixyNN: Energy-Efficient Real-Time Mobile Computer Vision Hardware Acceleration via Transfer LearningPaul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas K. Venkataramanaiah, Jae-sun Seo, Matthew Mattina. mlsys 2019: [doi]

2018

Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous VisionYuhao Zhu, Anand Samajdar, Matthew Mattina, Paul N. Whatmough. isca 2018: 547-560 [doi]

2008

2007

On-Chip Interconnection Architecture of the Tile ProcessorDavid Wentzlaff, Patrick Griffin, Henry Hoffmann, Liewei Bao, Bruce Edwards, Carl Ramey, Matthew Mattina, Chyi-Chang Miao, John F. Brown III, Anant Agarwal. micro, 27(5):15-31, 2007. [doi]

2006

Last level cache (LLC) performance of data mining workloads on a CMP - a case study of parallel bioinformatics workloadsAamer Jaleel, Matthew Mattina, Bruce L. Jacob. hpca 2006: 88-98 [doi]

2002

Tarantula: A Vector Extension to the Alpha ArchitectureRoger Espasa, Federico Ardanaz, Julio Gago, Roger Gramunt, Isaac Hernandez, Toni Juan, Joel S. Emer, Stephen Felix, P. Geoffrey Lowney, Matthew Mattina, André Seznec. isca 2002: 281 [doi]

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Publications by 'Matthew Mattina'

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2007

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2002

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