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