Overview
The Portenta H7 Lite is a cost-effective solution, designed for complex environments where radio communication is not suitable or possible. It is perfect for developers who want to leverage the computational power of the Portenta H7, without the need for video output or advanced security features.
The Portenta H7 Lite simultaneously runs high-level code and real-time tasks thanks to its two processors. For example, it can execute Arduino-compiled and MicroPython code at the same time, and have the two cores communicate with one another.
Key benefits include:
- Dual Core - Two best-in-class processors in one, running parallel tasks
- AI on the edge - So powerful it can run AI state machines
- Customization - The board is highly customizable in volumes
- High-level programming language support (Micropython)
The Portenta H7 Lite offers twofold functionality: it can run either like any other embedded microcontroller board, or as the main processor of an embedded computer.
For example, use the Portenta Vision Shield to transform your H7 Lite into an industrial camera capable of performing real-time machine learning algorithms on live video feeds. As the H7 Lite can easily run processes created with TensorFlow™ Lite, you could have one of the cores computing a computer vision algorithm on the fly, while the other carries out low-level operations like controlling a motor or acting as a user interface.
Portenta is the go-to family when performance is key, and the H7 Lite is no exception. We can already envision it as part of a wide range of solutions, including:
- High-end industrial machinery
- Laboratory equipment
- Computer vision
- PLCs
- Robotics controllers
- Mission-critical devices
- High-speed booting computation (ms)
Two Parallel Cores
The Portenta H7 Lite’s main processor is the STM32H747 dual core including a Cortex® M7 running at 480 MHz and a Cortex® M4 running at 240 MHz. The two cores communicate via a Remote Procedure Call mechanism that allows calling functions on the other processor seamlessly. Both processors share all the in-chip peripherals and can run:
- Arduino sketches on top of the Arm® Mbed™ OS
- Native Mbed™ applications
- MicroPython / JavaScript via an interpreter
- TensorFlow™ Lite
A New Standard for Pinouts
The Portenta family adds two 80-pin high-density connectors at the bottom of the board. This ensures scalability for a wide range of applications: simply upgrade your Portenta board to the one suiting your needs.
USB-C® Multipurpose Connector
The board’s programming connector is a USB-C port that can also be used to power the board, as a USB Hub, or to deliver power to OTG connected devices.
Arduino IoT Cloud Compatible
Use your MKR board on Arduino's IoT Cloud, a simple and fast way to ensure secure communication for all of your connected Things.
Need Help?
Check the Arduino Forum for questions about the Arduino Language, or how to make your own Projects with Arduino. If you need any help with your board, please get in touch with the official Arduino User Support as explained in our Contact Us page.
Warranty
You can find your board warranty information here.
Tech specs
Microcontroller |
STM32H747XI dual Cortex®-M7+M4 32bit low power Arm® MCU (datasheet) |
Secure Element (default) |
Microchip ATECC608 |
Board Power Supply (USB/VIN) |
5V |
Supported Battery |
Li-Po Single Cell, 3.7V, 700mAh Minimum (integrated charger) |
Circuit Operating Voltage |
3.3V |
Current Consumption |
2.95 μA in Standby mode (Backup SRAM OFF, RTC/LSE ON) |
Timers |
22x timers and watchdogs |
UART |
4x ports (2 with flow control) |
Ethernet PHY |
10 / 100 Mbps (through expansion port only) |
SD Card |
Interface for SD Card connector (through expansion port only) |
Operational Temperature |
-40 °C to +85 °C |
MKR Headers |
Use any of the existing industrial MKR shields on it |
High-density Connectors |
Two 80 pin connectors will expose all of the board's peripherals to other devices |
Camera Interface |
8-bit, up to 80 MHz |
ADC |
3× ADCs with 16-bit max. resolution (up to 36 channels, up to 3.6 MSPS) |
DAC |
2× 12-bit DAC (1 MHz) available, only one is accessible by the user through the external A6 pin |
USB-C |
Host / Device, High / Full Speed, Power delivery |
Conformities
Resources for Safety and Products
Manufacturer Information
The production information includes the address and related details of the product manufacturer.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
https://www.arduino.cc/
Responsible Person in the EU
An EU-based economic operator who ensures the product's compliance with the required regulations.
Arduino S.r.l.
Via Andrea Appiani, 25
Monza, MB, IT, 20900
Phone: +39 0113157477
Email: support@arduino.cc
Documentation
Learn more
Get Inspired
This project shows how to create a security system using the camera of an Arduino Nicla Vision board. The system automatically triggers a camera snapshot when presence is detected. Presence is detected when the system detects a sound level that exceeds a configurable threshold. The whole system is controlled by an Arduino Cloud dashboard.
Shortly after attending a recent tinyML workshop in Sao Paolo, Brazil, Joao Vitor Freitas da Costa was looking for a way to incorporate some of the technologies and techniques he learned into a useful project. Given that he lives in an area which experiences elevated levels of pickpocketing and automotive theft, he turned his attention to a smart car security system. His solution to a potential break-in or theft of keys revolves around the incorporation of an Arduino Nicla Vision board running a facial recognition model that only allows the vehicle to start if the owner is sitting in the driver’s seat. The beginning of the image detection/processing loop involves grabbing the next image from the board’s camera and sending it to a classification model where it receives one of three labels: none, unknown, or Joao, the driver. Once the driver has been detected for 10 consecutive seconds, the Nicla Vision activates a relay in order to complete the car’s 12V battery circuit, at which point the vehicle can be started normally with the ignition. Through this project, da Costa was able to explore a practical application of vision models at-the-edge to make his friend’s car safer to use. To see how it works in more detail, you can check out the video below and delve into the tinyML workshop he attended here.