Neuton.AI - No-code artificial intelligence for all

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.

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.

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.

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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

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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.

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.

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.

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.

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.

Evaluate the Uniqueness of Neuton's Approach by Comparing Benchmarks

Well-Known Magic Wand Case is 33 times Faster with Neuton

Total Footprint (kb)		TFLM	Neuton	Neuton Advantages
Flash	TinyML framework (model + inference engine + DSP)	79.96	5.42	14 times smaller
Flash	Device drivers and business logic	93.47	93.47	14 times smaller
SRAM	TinyML framework (model + inference engine + DSP)	18.2	1.72	10 times smaller
SRAM	Device drivers and business logic	45.69	45.69	10 times smaller
Inference time (us)		55 262	1 640	33 times faster
Holdout validation Accuracy		0.93	0.94	0,7% higher accuracy

Total Footprint (kb)	TFLM	Neuton
Flash
TinyML framework (model + inference engine + DSP)	79.96	5.42
Device drivers and business logic	93.47	93.47
Neuton Advantages	14 times smaller
SRAM
TinyML framework (model + inference engine + DSP)	18.2	1.72
Device drivers and business logic	45.69	45.69
Neuton Advantages	10 times smaller
Inference time (ms)	TFLM	Neuton
Inference time (ms)	55 262	1 640
Neuton Advantages	33 times faster
Holdout validation Accuracy	TFLM	Neuton
Holdout validation Accuracy	0.93	0.94
Neuton Advantages	0,7% higher occuracy

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Experiment details

In this experiment, we compare two models:

The trained TFLM Magic Wand model built into the Arduino SDK

The Neuton model trained on TF dataset from the Google repository

Both models are validated on the same holdout dataset (from the repository) and were tested on the same MCU (Arduino Nano 33 BLE Sense).

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Neuton vs. Non-neural Network Algorithms

This comparison illustrates the ability to achieve compact sizes while maintaining fast inference with the help of neural networks. This opens up wide opportunities for working with more complex tasks where non-neural network algorithms show worse performance.

Case	Holdout Accuracy			Inference Time, us			SRAM, kB			FLASH, kB
Case	Neuton	Non-neural network algorithms	More accurate	Neuton	Non-neural network algorithms	Times faster	Neuton	Non-neural network algorithms	Times smaller	Neuton	Non-neural network algorithms	Times smaller
Gearbox Fault Diagnosis	0.84	0.74	12%	56 776	84 080	1.5	3.54	4.95	1.3	9.64	11.15	1.1
Air-Writing Digits Recognition	0.93	0.82	12%	18 172	52 000	2.9	2.57	4.92	1.9	8.91	9.79	1.1
Arrhythmia Diagnostic binary	0.93	0.72	23%	5 212	106 220	20	0.98	1.9	1.9	2.59	8.61	3.3
Arrhythmia Diagnostic multi	0.96	0.77	20%	14 232	N/A	N/A	2.58	9.31	3.6	6.24	16.95	2.7

Holdout Accuracy

Case	Neuton	Non-neural network algorithms	More accurate
Gearbox Fault Diagnosis	0.84	0.74	12%
Air-Writing Digits Recognition	0.93	0.82	12%
Arrhythmia Diagnostic binary	0.93	0.72	23%
Arrhythmia Diagnostic multi	0.96	0.77	20%

Inference Time, us

Case	Neuton	Non-neural network algorithms	Times faster
Gearbox Fault Diagnosis	56 776	84 080	1.5
Air-Writing Digits Recognition	18 172	52 000	2.9
Arrhythmia Diagnostic binary	5 212	106 220	20
Arrhythmia Diagnostic multi	14 232	N/A	N/A

SRAM, kB

Case	Neuton	Non-neural network algorithms	Times smaller
Gearbox Fault Diagnosis	3.54	4.95	1.3
Air-Writing Digits Recognition	2.57	4.92	1.9
Arrhythmia Diagnostic binary	0.98	1.9	1.9
Arrhythmia Diagnostic multi	2.58	9.31	3.6

FLASH, kB

Case	Neuton	Non-neural network algorithms	Times smaller
Gearbox Fault Diagnosis	9.64	11.15	1.1
Air-Writing Digits Recognition	8.91	9.79	1.1
Arrhythmia Diagnostic binary	2.59	8.61	3.3
Arrhythmia Diagnostic multi	6.24	16.95	2.7

Realized on 8-bit MCU with AVR GNU Toolchain (7.3.0-atmel3.6.1-arduino7) & 16 MHz CPU frequency

Gearbox Fault Diagnosis

Check out how to apply the TinyML approach to detect broken tooth conditions in the gearbox. The dataset includes the vibration data recorded with 4 vibration sensors that were placed in 4 different directions, and under different load variations.

Download from GitHub

Air-Writing Digits Recognition

The goal is to correctly identify the digit that was written by a user in the air. The dataset contains 200 samples for each handwritten digit (from 0 to 9). Each sample was recorded for 2 seconds with a frequency of 100 Hz.

Download from GitHub

Arrhythmia Diagnostic

Binary Classification and Multiclass

Predict the possibility of cardiac arrhythmias and identify the type of arrhythmia based on the samples of ambulatory ECG recordings.

Download from GitHub

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

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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

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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

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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

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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

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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

Downloadable C library for Cortex M0 and Cortex M4

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.
Take advantage of all Zero Gravity features, multiplied by the following services:

Support package

Support 24/7

Free onboarding

Dedicated team: Data Scientist, Embedded Engineer, Project Manager

Service package

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

Platform access

Unlimited access for corporate users

Unlimited model creation

Downloadable C source code (C99 standard compliance)

Self-service for unlimited experiments

Send a Request

Blog

10.26.2022

Automated Design of Tiny Machine Learning Models: Part 2

Meet the long-awaited IEEE newsletter issue with the release of the second part of A Practical Guide with 3 full-cycle use cases illustrating the innovative Automated Design of Tiny Machine Learning Models.

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.

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