Elementor #4036 – Grovety

Integrate ML into Your Microcontrollers

We help overcome barriers to Embedded AI adoption

Make Trained Neural Networks Fit for Inference on Microcontrollers

Optimization by:

We do:

Transfer Learning on Microcontrollers:
On-Device Learning

In order to break down the barriers to using ML in Embedded: investment, complexity and development time, we offer a fine tunkey solution – GRC ML Module as an AI co-processor.

GRC AI Module

A self-learning engine doing Anomaly Detection and n-Class Classification for providing a turnkey AI functionality for integration into the products of various complexity.

GRC AI Module is an innovative and user-friendly solution for introducing AI functionality to your products in effortless and time-efficient manner. AI-based data processing offers a more flexible and powerful approach to analyzing and making decisions based the continuous data collection. Machine learning algorithms autonomously learn patterns and correlations from the time series data flow.

GRC AI Module is

A pre-installed firmware containing neural network based on the next-generation ODL Computing approach. It provides anomaly detection and classification of the input signals in accordance with the pre-trained data.
Intended for the use on ESP32 C3 FN4/H4 MCU - an optimal choice for the AI-related scenarios and Artificial Intelligence of Things (AIoT) due to the acceleration for the neural network computing and signal processing workloads provided by the SoC.

GRC AI-module

Development Board

The performance of an on-board neural network is done on the development board microcontroller by processing input data gathered from either connected or embedded sensors and learning on-the-fly. You do not have to be an AI expert, prepare a dataset, or train a model. AI functionality is available «out of the box»

Training and running of the neural network is performed on ESP32-C3

Dev Board User Guide

Dev Board Developer’s Guide (including code examples)

GRC Developer's Guide (including recommendations on embedding AI Module into other devices);

Comprehensive consultation in Edge AI questions:

Technologies

ML compilers

TFL+TFLµ; TVM+µTVM

SDK for inference

All available tools that simplify and speed up the design of the final solutions

Neural Network Optimization

For trained NNs

Compiler and hardware level

Runtime

Heterogeneous microcontroller architectures with NNA

On Device Learning (ODL)

The goal is to create more efficient smarter devices by figuring out changes in the current dataflow, so devices would autonomously adjust or reconfigure their operating model. If necessary, the “knowledge” gained by one device is shared with other connected devices.