84 citations · 91 across the 3 of their papers we have counts for
8 papers
Widening Access to Applied Machine Learning with TinyML
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy +21
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloe…
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…
Few-Shot Keyword Spotting in Any Language
Mark Mazumder, Colby Banbury, Josh Meyer +2
We introduce a few-shot transfer learning method for keyword spotting in any language. Leveraging open speech corpora in nine languages, we automate the extraction of a large multi…
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…
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
Robert David, Jared Duke, Advait Jain +10
Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges befo…
Visual Wake Words Dataset
Aakanksha Chowdhery, Pete Warden, Jonathon Shlens +2
The emergence of Internet of Things (IoT) applications requires intelligence on the edge. Microcontrollers provide a low-cost compute platform to deploy intelligent IoT application…