Neuton TinyML

Automatically compresses and optimizes neural network models for deployment on resource-constrained embedded devices without manual tuning or hyperparameter adjustment. Reduces model size and computational requirements while maintaining accuracy.

Deploys optimized machine learning models across multiple hardware platforms including microcontrollers, ARM processors, and mobile devices with minimal configuration. Automatically generates platform-specific code and binaries.

Enables retraining or fine-tuning of existing models with new data without starting from scratch. Preserves learned weights and adapts models to new data distributions or use cases.

Combines multiple trained models into an ensemble that leverages their collective predictions for improved accuracy and robustness. Automatically determines optimal weighting and combination strategies.

Reduces model precision from floating-point to lower-bit representations (8-bit, 4-bit, binary) while maintaining acceptable accuracy. Dramatically reduces model size and memory requirements.

Automatically searches for optimal hyperparameters and model configurations without manual tuning. Tests multiple parameter combinations and selects the best performing configuration.

Provides a visual, code-free interface for training machine learning models on structured data without requiring programming knowledge or ML expertise. Handles data preprocessing, feature engineering, and model selection automatically.

Automatically evaluates trained models and generates performance metrics including accuracy, precision, recall, and other relevant statistics. Provides visualization and comparison of model performance across different configurations.

Imports datasets from various sources and automatically handles data cleaning, normalization, and formatting for ML model training. Detects and handles missing values, outliers, and data type conversions.

Optimizes models specifically for minimal file size and fast inference latency on edge devices. Provides trade-off analysis between model accuracy, size, and inference speed.

Analyzes and visualizes which features or input variables have the most impact on model predictions. Helps identify which data points are most important for the model's decision-making.

Tracks and manages multiple versions of trained models with their configurations, performance metrics, and deployment history. Enables comparison and rollback between model versions.

Automatically generates inference code in C, C++, Python, or other languages for running predictions with deployed models. Code is optimized for target hardware and includes necessary libraries and dependencies.

Executes trained models on edge devices to generate predictions in real-time from sensor data or input streams. Provides low-latency inference suitable for time-sensitive applications.