9.7k citations · 11.1k across the 15 of their papers we have counts for
16 papers
Moonshine: Speech Recognition for Live Transcription and Voice Commands
Nat Jeffries, Evan King, Manjunath Kudlur +3
This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing. Moonshine is based on an encoder-decoder trans…
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Colby Banbury, Emil Njor, Andrea Mattia Garavagno +7
Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tas…
Materiality and Risk in the Age of Pervasive AI Sensors
Mona Sloane, Emanuel Moss, Susan Kennedy +4
Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable implications for a range of AI risks, including to privacy, the env…
Datasheets for Machine Learning Sensors
Matthew Stewart, Yuke Zhang, Pete Warden +12
Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These "ML sensors" enable context-sensitive, real-time data collection and decision-making across divers…
Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
Shvetank Prakash, Matthew Stewart, Colby Banbury +4
The sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment's future. The growing Internet of Things (IoT) has the pot…
Machine Learning Sensors
Pete Warden, Matthew Stewart, Brian Plancher +6
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from c…
CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
Shvetank Prakash, Tim Callahan, Joseph Bushagour +5
Need for the efficient processing of neural networks has given rise to the development of hardware accelerators. The increased adoption of specialized hardware has highlighted the…
ML-EXray: Visibility into ML Deployment on the Edge
Hang Qiu, Ioanna Vavelidou, Jian Li +5
Benefiting from expanding cloud infrastructure, deep neural networks (DNNs) today have increasingly high performance when trained in the cloud. Researchers spend months of effort c…
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…
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Pete Warden
Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. Discusses why this task is an interesting challenge, and why it requires a…
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi, Ashish Agarwal, Paul Barham +37
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed…