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20192026
most citedBenchmarking TinyML Systems: Challenges and Direction

200 citations · 616 across the 14 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CL2021

LSH methods for data deduplication in a Wikipedia artificial dataset

Juan Ciro, Daniel Galvez, Tim Schlippe +1

This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset. To evaluate the different models, we cr…

cs.LG2021★ 6 cited

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

Daniel Galvez, Greg Diamos, Juan Ciro +7

The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-B…

cs.LG2021★ 3 cited

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Steven Farrell, Murali Emani, Jacob Balma +40

Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…

cs.LG2021★ 173 cited

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39

Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…

cs.LG2021★ 2 cited

MLPerf Tiny Benchmark

Colby Banbury, Vijay Janapa Reddi, Peter Torelli +19

Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the…

cs.LG2021★ 5 cited

Data Engineering for Everyone

Vijay Janapa Reddi, Greg Diamos, Pete Warden +2

Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But ML requires more data…