Please fill out the form below to register to the ST AI Pitch Challenge!


Through the ST Up initiative the ST Microcontroller Division (MCD) is looking for applications, partners capable to demonstrate how to use AI, Machine Learning and especially Neural networks technics on our portfolio.

Since beginning of 2019 ST MCD is proposing an ecosystem making it easier to run Neural Networks on our microcontroller named STM32Cube.AI. Our goal is to focus on smart sensing, computing at the node/edge.

Our portfolio is made of wide-range of microcontroller products from robust, low-cost 8-bit MCUs up to 32-bit Arm®-based Cortex®-M Flash microcontrollers with a comprehensive choice of peripherals. It also features wireless connectivity solutions including our new ultra-low-power, dual-core STM32WB microcontroller series. This multi-protocol wireless MCU platform is able to run Bluetooth™ 5, OpenThread, ZigBee 3.0 and IEE 802.15.4 communication protocols concurrently. With the addition of the STM32 Microprocessor (MPU) and its heterogeneous architecture combining Arm® Cortex®-A and Cortex®-M Cores, embedded system engineers are given new design possibilities and access to open-source Linux and Android platforms.

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Are you part of... ?

Indicate the sector of interest by ticking the box*
Industry 4.0:
Anomaly detectionSmart FactoryRobotics and automation

Smart infrastructures Cities/BuildingsHomesOffices

Medical/HealthcareSport/ activity monitoringHealth parameters monitoringChildren, elder people monitoring

Smart agricultureSmart sensing for monitoringAgricultural machines

Home ApplianceAnomaly detectionSmart control

Toys MarketAutomationHuman to machine interaction


Our focus is on edge computing for any kind of processing:

Please check at least one of the boxes below and detail the parameters right under

Low data bandwidth processing like motion sensors, vibrations, sounds, temperature, humidity, pressure, …

1. Cost effective solutions

2. Ultra low power (Battery operated)

3. Integrated

4. Including Low power connectivity

Camera/computer vision use cases

1. Cost effective solutionss

2. Low power

AI Secured implementation

Security with AI

Distributed AI

Please check at least one of the boxes below and detail the parameters right under

Part of the processing perform locally with embedded Neural networks and second part performed on the cloud.

Capability from the cloud to update the Node/Edge inference engine