Neuton.AI - No-code artificial intelligence for all

Exceptionally Tiny Models without Loss of Accuracy

Explore a novel approach to bringing extra capabilities to edge devices

Less than

1 KB

is an average model size achieved by our neural framework

Even tiny

8-bit

microcontrollers can be AI-driven and run robust neural networks

Up to

1,000 times

smaller models compared to other existing algorithms

Only

1 iteration

is enough to build an accurate, fast and incredibly tiny model

No-Code Automated TinyML Platform

Learn how in just 2 minutes

Neuton is a no-code Tiny AutoML platform empowered by a patented Neural Network Framework under the hood. Offering a highly automated and transparent pipeline, Neuton doesn't require a lot of actions from the user's side. It helps to automatically build extremely compact and accurate models without additional compression and natively embed them into 8, 16, and 32-bit MCUs!

Free for Developers

Build Extremely Tiny Models. Run Them on the Smallest IoT Devices.

Create Smart IoT Devices by Leveraging TinyML

Predictive Maintenance

Predictive Maintenance of Compressor Water Pumps

Ever thought about how water enters the central heating pipes? The secret is the proper operation of water pumps. Learn how to use RSL10 sensors and Neuton to run models for timely pump maintenance.

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Smart Human Interfaces

Handmade Drawing Recognition Interface as from my Smartphone

How to quickly develop TinyML models to recognize custom, user-defined drawn gestures on touch interfaces? Check out the project inspired by a gesture recognition feature on a simple smartphone.

Learn more

Condition Monitoring

Detecting Unstable Electrical Grid with TinyML

Electricity permeates the entire infrastructure of modern cities, so it's really important to monitor and prevent overloads. Explore how to predict electrical grid stability with Neuton and Particle IoT.

Learn more

Explore All Projects

Neuton – the First Neural Network Framework Designed to Create Models with Minimal Size and without Loss of Accuracy

Neuton is based on a unique patented machine learning algorithm that forgoes error backpropagation and stochastic gradient descent, growing the network structure neuron by neuron. This helps to build neural networks without compromising between accuraсy and size:

with the excellent generalizing capability

with minimal size, often less than 1 Kb

without loss of accuracy

without compression techniques

Read Article

5 Unique Features of Neuton Network Framework

Selective connections

Unique algorithm

Automatic structure growth

No manual search

Constant cross-validation

Selective approach to the connected features

A selective approach to the connected features helps to establish only necessary neuron connections while creating a model, not allowing the structure to grow randomly.

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Unique patented global optimization algorithm

Neuton's algorithm enables the platform not to base its computation on error backpropagation and stochastic gradient descent. It provides the opportunity to successfully create compact models, minimizing the likelihood of hitting local minima.

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Automatic neuron-by-neuron structure growth

The principle of neuron-by-neuron structure growth helps Neuton to learn from the general features to the most specific ones, and select the optimal model size and high accuracy, required to solve the task.

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No manual search for neural network parameters

Neuton eliminates the time-consuming random manual search for neural network parameters (the number of layers and neurons in a layer, type of activation function, batch size, learning rate, etc.), and allows you to quickly and efficiently find the optimal structure.

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Constant cross-validation

The step-by-step growth of the neural network makes it possible to cross-validate with the addition of each neuron and to increase the generalizing capabilities of the model.

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Evaluate the Uniqueness of Neuton's Approach

by Comparing Benchmarks

For example, Neuton's Model for Combined Cycle Power Plant Data Set is 208.6 times smaller than TensorFlow, and 42.3 times smaller than TensorFlow Lite.

Algorithm

Size, kB

Coefficients

Target metric

Holdout metric

TensorFlow

31.08

2 338

MAE

3.35

TensorFlow Lite

6.31

n/a

MAE

3.36

Neuton

0.149

100

MAE

3.30

Combined Cycle Power Plant Data Set

The dataset contains 9,568 points collected from a Combined Cycle Power Plant over 6 years. Features consist of hourly average ambient variables (Temperature, Ambient Pressure, Relative Humidity and Exhaust Vacuum) used to predict the net hourly electrical energy output.

Explore Benchmarks

With Neuton.ai, you only need to press the button to turn data into a model. Tools like these enable almost anyone to get results.

Massimo Banzi

Co-founder at Arduino

Neuton created ultra-tiny deployable machine learning models with footprints of a few kilobytes. While these results fit comfortably MCUs such as STM32, they are essential to run in-sensor neural computing workloads.

Danilo Pietro Pau

Technical Director, IEEE and ST Fellow; System Research and Applications, STMicroelectronics

As one of the key players in Arm's partner ecosystem, Neuton empowers developers to successfully go through the entire lifecycle of bringing machine learning to the edge without any deep technical knowledge.

George Anadiotis

Contributing Writer at ZDNet

You need to find ways to save time and make model creation run faster, and AutoML tools like Neuton are incredible, as you spend a lot less time on feature engineering, tuning the hyperparameters, evaluating the performance, and other iterations.

Fabio Correa

Director of Business Analytics at Microsoft

A strong, thriving ecosystem is critical to delivering new AI solutions for the next decade of compute. The work Neuton is doing will enable even more developers to unlock new opportunities through ML at the edge.

Mary Bennion

Director of Ecosystem at Arm

Neuton Pricing and Options

Zero Gravity

Self-service free unlimited plan.

Enterprise Plan

Get assisted and supported

Neuton Services are absolutely free of charge. Get up to $500 in credits for infrastructure costs on Google Cloud Platform.

Training

Unlimited number of models

Data Preprocessing

Feature Engineering

Prediction

Unlimited number of predictions via the web interface, REST API or downloaded models

Explainability

Exploratory Data Analysis

Model Interpreter

Model Quality Diagram

Model Decay Indicator

Infrastructure

Only pay for what you use. Google Cloud Platform Costs

Minimum 100 hours of training included

$0/mo

Use for Free

Enterprise Plan is ideal for launching large-scale IoT projects as it combines an individual approach and a full cycle of end-to-end data science services, executed on the most unique patented technology on the market. Neuton's experienced tech team guarantees the most accurate, compact, and fastest ML solution possible based on client's specific needs and goals.

Support

Support 24/7

Free onboarding

Dedicated team: Data Scientist, Embedded Engineer, Project Manager

Comprehensive approach

Revealing the potential of AI

Identifying the most important AI cases

Identifying the necessary data for the use case

Data preparation, feature engineering

Building an optimal model, evaluating its quality and size

Identifying the most important factors and data-driven insights

Embedding a model into the device

Evaluating business results

We will train client's team to

Make AI-driven decisions within days, using a no-code AutoML platform

Identify the necessary data for building a model

Build compact models automatically and evaluate their quality and size

Find data insights and identify hidden patterns

Embed models into the tiniest Edge devices

Manage the lifecycle of a model and update it

Send a Request

Blog

07.26.2022

Arm Tech Talk: Solving Real-World Challenges with Arm and Neuton

Neuton.AI, represented by our embedded engineer, Sumit Kumar, will participate in the new Arm’s AI Tech Talk on September 20th, at 8:00 AM PT. You don't want to miss this!

07.18.2022

Automated Design of Tiny Machine Learning Models: Part 1

Together with colleagues from STM, Neuton's team shares a new approach to creating TinyML models in the article, published in the latest IEEE newsletter issue.

07.1.2022

Our First Neuton Maker Day and a Cool Competition for Makers

We launched our very first Neuton Maker Day! The webinar brought together five talented makers who shared their unique ready-to-go tinyML projects developed with the Neuton platform.

Neuton TinyML

Neuton
TinyML