Add to ORKGRecent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an entirely new class of edge applications. However, continued progress is restrained by the lack of benchmarking Machine Learning (ML) models on TinyML hardware, which is fundamental to this field reaching maturity. In this paper, we designed 3 types of fully connected Neural Networks (NNs), trained each NN using 10 datasets (produces 30 NNs), and present the benchmark by reporting the onboard model performance on 7 popular MCUboards (similar boards are used to design TinyML hardware). We open-sourced and made the complete benchmark results freely available online 1 to enable the TinyML community researchers and developers to systematically com...
We use 250 billion microcontrollers daily in electronic devices that are capable of running machine ...
TinyML är ett snabb växande tvärvetenskapligt område i maskininlärning. Den fokuserar på att möjligg...
Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system appli...
Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an...
The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to...
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an enti...
With the surge of inexpensive computational and memory resources, neural networks (NNs) have experie...
In this current technological world, the application of machine learning is becoming ubiquitous. Inc...
The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typical...
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has b...
Tiny Machine Learning (TML) is a novel research area aiming at designing and developing Machine Lear...
Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed i...
This paper describes the progress made in the context of a research and development project on machi...
Tiny Machine Learning (TinyML) is an expanding research area based on pushing intelligence to the ed...
Machine Learning (ML) functions are becoming ubiquitous in latency- and privacy-sensitive IoT applic...
We use 250 billion microcontrollers daily in electronic devices that are capable of running machine ...
TinyML är ett snabb växande tvärvetenskapligt område i maskininlärning. Den fokuserar på att möjligg...
Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system appli...
Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to unlock an...
The field of Tiny Machine Learning (TinyML) has gained significant attention due to its potential to...
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an enti...
With the surge of inexpensive computational and memory resources, neural networks (NNs) have experie...
In this current technological world, the application of machine learning is becoming ubiquitous. Inc...
The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typical...
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has b...
Tiny Machine Learning (TML) is a novel research area aiming at designing and developing Machine Lear...
Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed i...
This paper describes the progress made in the context of a research and development project on machi...
Tiny Machine Learning (TinyML) is an expanding research area based on pushing intelligence to the ed...
Machine Learning (ML) functions are becoming ubiquitous in latency- and privacy-sensitive IoT applic...
We use 250 billion microcontrollers daily in electronic devices that are capable of running machine ...
TinyML är ett snabb växande tvärvetenskapligt område i maskininlärning. Den fokuserar på att möjligg...
Machine Learning (ML) on the edge is key for enabling a new breed of IoT and autonomous system appli...
