Generalized image warping using enhanced lookup tables. - Peter Mattson; Donglok Kim; Yongmin Kim 001 (Int. J. Imaging Syst. Technol., 1998)

Interactive Image Morphing on a Single-Chip Multiprocessor using a Multilayered Parallel Image Computing Library. - Peter Mattson; Chris Basoglu; Yongmin Kim 001 (Real Time Imaging, 2000)

ML Benchmark Design Challenges. - Peter Mattson (Hot Chips Symposium, 2019)

Scale MLPerf-0.6 models on Google TPU-v3 Pods. - Sameer Kumar; Victor Bitorff; Dehao Chen; Chiachen Chou; Blake A. Hechtman; HyoukJoong Lee; Naveen Kumar; Peter Mattson; Shibo Wang; Tao Wang; Yuanzhong Xu; Zongwei Zhou (CoRR, 2019)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Cody Coleman; Greg Diamos; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (CoRR, 2019)

MLPerf Inference Benchmark. - Vijay Janapa Reddi; Christine Cheng; David Kanter; Peter Mattson; Guenther Schmuelling; Carole-Jean Wu; Brian Anderson; Maximilien Breughe; Mark Charlebois; William Chou; Ramesh Chukka; Cody Coleman; et al. (ISCA, 2020)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Gregory F. Diamos; Cody Coleman; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (MLSys, 2020)

MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It. - Vijay Janapa Reddi; David Kanter; Peter Mattson; Jared Duke; Thai Nguyen; Ramesh Chukka; Kenneth Shiring; Koan-Sin Tan; Mark Charlebois; William Chou; Mostafa El-Khamy; Jungwook Hong; et al. (CoRR, 2020)

MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance. - Peter Mattson; Hanlin Tang; Gu-Yeon Wei; Carole-Jean Wu; Vijay Janapa Reddi; Christine Cheng; Cody Coleman; Greg Diamos; David Kanter; Paulius Micikevicius; David A. Patterson 001; Guenther Schmuelling (IEEE Micro, 2020)

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (MLHPC@SC, 2021)

Multilingual Spoken Words Corpus. - Mark Mazumder; Sharad Chitlangia; Colby R. Banbury; Yiping Kang; Juan Ciro; Keith Achorn; Daniel Galvez; Mark Sabini; Peter Mattson; David Kanter; Greg Diamos; Pete Warden; et al. (NeurIPS Datasets and Benchmarks, 2021)

Data Engineering for Everyone. - Vijay Janapa Reddi; Greg Diamos; Pete Warden; Peter Mattson; David Kanter (CoRR, 2021)

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation. - Alexandros Karargyris; Renato Umeton; Micah J. Sheller; Alejandro Aristizabal; Johnu George; Srini Bala; Daniel J. Beutel; Victor Bittorf; Akshay Chaudhari; Alexander Chowdhury; Cody Coleman; Bala Desinghu; et al. (CoRR, 2021)

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (CoRR, 2021)

The Vision Behind MLPerf: Understanding AI Inference Performance. - Vijay Janapa Reddi; Christine Cheng; David Kanter; Peter Mattson; Guenther Schmuelling; Carole-Jean Wu (IEEE Micro, 2021)

Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks. - Tristan Thrush; Kushal Tirumala; Anmol Gupta; Max Bartolo; Pedro Rodriguez 001; Tariq Kane; William Gaviria Rojas; Peter Mattson; Adina Williams; Douwe Kiela (ACL (demo), 2022)

MLPerf Mobile Inference Benchmark: An Industry-Standard Open-Source Machine Learning Benchmark for On-Device AI. - Vijay Janapa Reddi; David Kanter; Peter Mattson; Jared Duke; Thai Nguyen; Ramesh Chukka; Kenneth Shiring; Koan-Sin Tan; Mark Charlebois; William Chou; Mostafa El-Khamy; Jungwook Hong; et al. (MLSys, 2022)

Metrics reloaded: Pitfalls and recommendations for image analysis validation. - Lena Maier-Hein; Annika Reinke; Evangelia Christodoulou; Ben Glocker; Patrick Godau; Fabian Isensee; Jens Kleesiek; Michal Kozubek 001; Mauricio Reyes 001; Michael A. Riegler; Manuel Wiesenfarth; Michael Baumgartner 001; et al. (CoRR, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Douwe Kiela; David Jurado; David Kanter; Rafael Mosquera; et al. (CoRR, 2022)

DataPerf: Benchmarks for Data-Centric AI Development. - Mark Mazumder; Colby R. Banbury; Xiaozhe Yao; Bojan Karlas; William Gaviria Rojas; Sudnya Frederick Diamos; Greg Diamos; Lynn He; Alicia Parrish; Hannah Rose Kirk; Jessica Quaye; Charvi Rastogi; et al. (NeurIPS, 2023)

Understanding metric-related pitfalls in image analysis validation. - Annika Reinke; Minu Tizabi; Michael Baumgartner 001; Matthias Eisenmann; Doreen Heckmann-Nötzel; A. Emre Kavur; Tim Rädsch; Carole H. Sudre; Laura Ación; Michela Antonelli; Tal Arbel; Spyridon Bakas; et al. (CoRR, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

DMLR: Data-centric Machine Learning Research - Past, Present and Future. - Luis Oala; Manil Maskey; Lilith Bat-Leah; Alicia Parrish; Nezihe Merve Gürel; Tzu-Sheng Kuo; Yang Liu 018; Rotem Dror; Danilo Brajovic; Xiaozhe Yao; Max Bartolo; William Gaviria Rojas; et al. (CoRR, 2023)

Federated benchmarking of medical artificial intelligence with MedPerf. - Alexandros Karargyris; Renato Umeton; Micah J. Sheller; Alejandro Aristizabal; Johnu George; Anna Wuest; Sarthak Pati; Hasan Kassem; Maximilian Zenk; Ujjwal Baid; Prakash Narayana Moorthy; Alexander Chowdhury; et al. (Nat. Mac. Intell., 2023)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Pieter Gijsbers; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; et al. (DEEM@SIGMOD, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Luca Foschini 002; Joan Giner-Miguelez; Pieter Gijsbers; Sujata S. Goswami; Nitisha Jain; Michalis Karamousadakis; Michael Kuchnik; Satyapriya Krishna; Sylvain Lesage; et al. (NeurIPS, 2024)

Croissant: A Metadata Format for ML-Ready Datasets. - Mubashara Akhtar; Omar Benjelloun; Costanza Conforti; Joan Giner-Miguelez; Nitisha Jain; Michael Kuchnik; Quentin Lhoest; Pierre Marcenac; Manil Maskey; Peter Mattson; Luis Oala; Pierre Ruyssen; et al. (CoRR, 2024)

Introducing v0.5 of the AI Safety Benchmark from MLCommons. - Bertie Vidgen; Adarsh Agrawal; Ahmed M. Ahmed 004; Victor Akinwande; Namir Al-Nuaimi; Najla Alfaraj; Elie Alhajjar; Lora Aroyo; Trupti Bavalatti; Borhane Blili-Hamelin; Kurt D. Bollacker; Rishi Bomassani; et al. (CoRR, 2024)

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons. - Shaona Ghosh; Heather Frase; Adina Williams; Sarah Luger; Paul Röttger; Fazl Barez; Sean McGregor; Kenneth Fricklas; Mala Kumar; Quentin Feuillade-Montixi; Kurt Bollacker; Felix Friedrich; et al. (CoRR, 2025)