Overview
Portenta C33 is a streamlined module that offers the high performance of the Portenta family at a lower price point, thanks to optimizations and streamlined features.
Ideal to develop cost-effective, real-time applications, Portenta C33 features the Arm® Cortex®-M33 microcontroller by global leader Renesas and supports MicroPython and other high-level programming languages. Thanks to its onboard Wi-Fi® and Bluetooth® Low Energy connectivity, the module stands out as an ideal solution for IoT gateways, remote control systems, fleet management and process tracking.
While its secure element guarantees industrial-grade security at the hardware level, the Portenta C33 is also able to perform over-the-air firmware updates with Arduino IoT Cloud or other third-party services.
Quickly deploying AI-powered projects becomes quick and easy with Portenta C33, by leveraging a vast array of ready-to-use software libraries and Arduino sketches available, as well as widgets that display data in real time on Arduino IoT Cloud-based dashboards. What’s more, the module’s form factor is compatible with the Portenta and MKR ranges and features castellated pins – ready for automatic assembly lines and more efficient connections to other components.
Key benefits include:
- Ideal for low-cost IoT applications with Wi-Fi®/Bluetooth® LE connectivity
- Supports MicroPython and other high-level programming languages
- Offers industrial-grade security at the hardware level and secure OTA firmware updates
- Leverages ready-to-use software libraries and Arduino sketches
- Perfect to monitor and display real-time data on Arduino IoT Cloud widget-based dashboards
- Compatible with Arduino Portenta and MKR families
- Features castellated pins for automatic assembly lines
Cost Effective Performance
Reliable, secure and with computational power worthy of its range, Portenta C33 was designed to provide big and small companies in every field with the opportunity to access IoT and benefit from higher efficiency levels and automation.
Applications
Portenta C33 brings more applications than ever within users’ reach, from enabling quick plug-and-play prototyping to providing a cost-effective solution for industrial-scale projects.
Applications include:
- Industrial IoT gateway
- Machine monitoring to track OEE/OPE
- Inline quality control and assurance
- Energy consumption monitoring
- Appliances control system
- Ready-to-use IoT prototyping solution
For more information, see the Portenta C33 product page and feel free to get in touch with our Sales Engineers.
For full documentation and complete technical specs, visit Arduino Docs.
Tech specs
| Microcontroller | Renesas R7FA6M5BH2CBG Arm® Cortex®-M33:
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| External Memories | 16 MB QSPI Flash |
| USB-C® | USB-C® High Speed |
| Connectivity |
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| Interfaces |
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| Security | NXP® SE050C2 Secure Element |
| Operating Temperatures | -40 °C to +85 °C (-40 °F to 185 °F) |
| Dimensions | 66,04 mm x 25,40 mm |
Documentation
Learn more
Get Inspired
Begins a process of combining Portenta H7 with Vision Shield – LoRa by acquiring, sending, and displaying a camera image via USB/Serial connection.
Jeremy Ellis is a teacher, and as such, wanted a longer-term project that his students could do to learn more about microcontrollers and computer vision/machine learning, and what better way is there than a self-driving car. His idea was to take an off-the-shelf RC car which uses DC motors, add an Arduino Portenta H7 as the MCU, and train a model to recognize target objects that it should follow. After selecting the “RC Pro Shredder” as the platform, Ellis implemented a VNH5019 Motor Driver Carrier, a servo motor to steer, and a Portenta H7 + Vision Shield along with a 1.5” OLED module. After 3D printing a small custom frame to hold the components in the correct orientation, nearly 300 images were collected of double-ringed markers on the floor. These samples were then uploaded to Edge Impulse and labeled with bounding boxes before a FOMO-based object detection model was trained. Rather than creating a sketch from scratch, the Portenta community had already developed one that grabs new images, performs inferencing, and then steers the car’s servo accordingly while optionally displaying the processed image on the OLED screen. With some minor testing and adjustments, Ellis and his class had built a total of four autonomous cars that could drive all on their own by following a series of markers on the ground. For more details on the project, check out Ellis' Edge Impulse tutorial here.