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Found 15 papers, 9 papers with code
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Training with Wake Vision improves accuracy by 1. 93% over existing datasets, demonstrating the importance of dataset quality for low-capacity models and dataset size for high-capacity models.
Datasheets for Machine Learning Sensors: Towards Transparency, Auditability, and Responsibility for Intelligent Sensing
Machine learning (ML) sensors are enabling intelligence at the edge by empowering end-users with greater control over their data.
Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
This article discusses both the potential of these TinyML applications to address critical sustainability challenges, as well as the environmental footprint of this emerging technology.
Machine Learning Sensors
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications.
CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
In this paper, we present CFU Playground: a full-stack open-source framework that enables rapid and iterative design and evaluation of machine learning (ML) accelerators for embedded ML systems.
ML-EXray: Visibility into ML Deployment on the Edge
The key challenge is that there is not much visibility into ML inference execution on edge devices, and very little awareness of potential issues during the edge deployment process.
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation.
Few-Shot Keyword Spotting in Any Language
With just five training examples, we fine-tune the embedding model for keyword spotting and achieve an average F1 score of 0. 75 on keyword classification for 180 new keywords unseen by the embedding model in these nine languages.
Data Engineering for Everyone
This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations.
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
We introduce TensorFlow Lite Micro (TF Micro), an open-source ML inference framework for running deep-learning models on embedded systems.
Visual Wake Words Dataset
To facilitate the development of microcontroller friendly models, we present a new dataset, Visual Wake Words, that represents a common microcontroller vision use-case of identifying whether a person is present in the image or not, and provides a realistic benchmark for tiny vision models.
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems.
Ranked #1 on
Keyword Spotting
on TensorFlow
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
