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READY FOR PROJECT START
phyBOARD®-Pollux AI kit
- Reference design for the development of simple to complex AI applications
- Development examples for face and object recognition are included.
- Reference platform with NXP i.MX 8M Plus, phyCAM-M camera and optional display
- Additional add-ons for increased AI performance via Hailo or Ara240 DNPU
- Step-by-step instructions for creating facial recognition software
- Ready-to-use Full Feature Board Support Package (BSP)
- Easy integration of the AI toolset into your own Yocto BSPs
- NXP eIQ® Machine Learning Software integrated into the BSP
- Using the NPU directly via driver API, Python and GStreamer
phyBOARD-Pollux AI Kit for Edge AI Development
The phyBOARD-Pollux AI Kit serves as both a learning platform and a reference design for unlocking the embedded vision and machine learning capabilities of the NXP i.MX 8M Plus processor. It is specifically designed for developing simple to complex AI applications and includes all the necessary hardware and software components for object and face recognition applications. The AI computing power can also be expanded with Hailo or Ara240 DNPUs as needed via our add-ons.
AI Use Case: Object and Face Recognition
The kit is based on the phyCORE-i.MX 8M Plus System on Module (SoM), which leverages the capabilities of the NXP i.MX 8M Plus processor. With its quad-core Arm Cortex (1,6 GHz), integrated NPU with 2,3 TOPs of performance, and HDR-capable Image Signal Processor (ISP), the NXP processor is ideal for object and face recognition applications. The necessary camera is included: The proven phyCAM-M camera system consists of a cost-effective and industrial-grade VM-016 HD camera with 1 MP resolution and a MIPI-CSI-2 interface, an M12 lens, and a lens holder. Image data is captured and processed directly by the integrated ISP. The advantage: No post-processing of the image data is required.
Demo application for facial recognition included
The provided demo application facilitates the use of neural networks for object comparison using machine and deep learning algorithms. Specific facial features are extracted from live-recorded images of faces using various neural networks. These features are then compared to pre-trained images of faces belonging to celebrities. The face with the fewest deviations from the user's own face is displayed as the best match.
Software ecosystem
Integration into your own BSPs is designed to be as simple as possible. You can also use the ready-to-use, full-feature BSP provided by Phytec, which includes the development environment for NXP's eIQ.®Machine learning software is also already integrated. Expanding AI performance with additional DNPUs is also easily possible via driver API, Python, and GStreamer.
| Ethernet | 2x 10/100/1000 Mbit/s |
| Wireless | 802.11 b/g/n (ac) 2.4 GHz / 5 GHz (via optional Wi-Fi Adapter) |
| Bluetooth | BLE 4.2 |
| USB | 2x 3.0 Host |
| Serial | 1x RS-232/RS-485 Full Duplex/RS-485 Half Duplex |
| CAN | 2x CAN FD |
| Digital I/O | optional via Expansion Connector |
| PCle | 1x miniPCIe |
| Mass Memory | microSD Card Slot |
| Display | LVDS (1x 4 lanes or 1x 8 lanes), MIPI DSI (1x 4 lanes), HDMI |
| Audio | SAI via A/V Connector |
| Camera | 2x MIPI CSI-2 (phyCAM-M) |
| Expansion Bus | I²C, SPI, SDIO (8-bit), UART, JTAG, USB |
| JTAG | via Expansion Connector |
| User Control Elements | 1x Reset Button, 1x On/Off, 1x Boot source select |
| Boot Source | eMMC, SD Card, NOR, USB Serial Download, Internal Fuses |
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