Battery-Powered Edge AI Module on Himax HX6538 with Cortex-M55 & Ethos-U55 | Open-Source Platform

Who is it for?

The GRC AI Module is designed for developers, hardware startups, and product teams who need a fast, reliable way to build battery-powered edge AI solutions. It's ideal for use in wildlife monitoring, license plate recognition, security systems, mobile robotics, and other smart sensing applications.

 

Ready-to-use Example Applications

Gate — Standalone ANPR Module for Gate & Barrier

A fully autonomous, battery-friendly module that opens your barrier or gate when it detects a recognized license plate.

Wild — autonomous long-term AI camera trap

Battery-powered AI camera trap for wildlife monitoring. Detects animal and bird species using onboard neural networks — no cloud or internet required.

What can you do with it?

With this module, you can:

• Run neural networks for object detection and classification (e.g., animals, vehicles, license plates);
• Capture and process audio and video directly on the device;
• Develop fully autonomous systems that run on batteries or rechargeable power;
• Integrate it as a drop-in component into your final product — for example, a wildlife camera trap, smart garage door opener, or portable analyzer.

Key benefits

• Ready-to-use platform: Includes camera interface, microphone array, uSD storage, ToF sensor, IMU, and Bluetooth — all pre-integrated.
• Open-source stack: Full firmware, Android app, and documentation available to accelerate development and customization.
• Ultra-low power: Based on the Arm Cortex-M55 and Ethos-U55, ideal for battery-operated and always-on applications.
• Fast time to market: Use the module as a finished component — build your enclosure, add your model, and go to production.

Open-source projects for rapid product development

Examples:

• Wildlife camera trap — animal/bird species recognition;
• License plate recognition (LPR/ANPR);
• Smart garage door opener
• Custom object detection/classification systems.

An open-source object detection and classification project that you can customize for your specific use case

End-to-End Development Workflow

1. Platform Evaluation

• You have a pre-configured dev kit 
  (hardware + firmware + pre-trained model)
• Run out-of-the-box demo (e.g., animal detection) to test:
  • Inference performance (FPS, accuracy)
  • API integration (REST/gRPC/edge SDK)
  • Hardware compatibility (CPU/GPU/NPU utilization)
• Review the API specification and model compatibility guidelines via the pre-built demo

2. Custom Model Integration

Swap the default demo neural network for a task-specific model 

(e.g., ANPR for local license plates) via:

• DIY path: adapt existing models;
• White-glove service: Grovety delivers a custom model (optimized for target hardware).

3. Prototype to Production

• Modify the open-source board firmware to implement custom business logic;
• Enhance the mobile app with required features and UI/UX changes;
• Finalize the hardware: uses existing enclosure or develops custom hardware.

Outcome: A ready-to-sell solution with low development costs and short time-to-market.

Subsystems & Their Functions

Under Himax control

• Image capture from camera module
• Audio acquisition from 4-microphone array
• Image processing using built-in hardware accelerators
• Image/audio processing using neural networks and Arm Ethos
• Data exchange with nRF52833 via SPI/I2C interfaces
• microSD card operations

Under nRF52833 control

• Himax communication via SPI and/or I2C interfaces
• Host communication via Bluetooth and/or USB-UART interfaces
• Power management and device configuration 
• (Himax, accelerometer, ToF sensor)
• Battery charging control and state monitoring
• LED control and button state reading

Hardware Components List

 1. Processing Units

• Main MCU: HiMax HX6538 – Handles primary tasks:
  • Neural network acceleration (Ethos-U55 NPU)
  • Camera interfacing (MIPI CSI-2)
  • Microphone array processing (PDM/PCM)
  • uSD card storage management

• Peripheral MCU: Nordic nRF52833 – Manages:
  • Bluetooth 5.2 Low Energy connectivity
  • Power management (battery/DC-in)
  • Time-of-Flight (ToF) sensor data acquisition
  • Inertial Measurement Unit (IMU) processing

2. Sensors & Peripherals

• Imaging:
  • Camera-In (MIPI interface) – Primary vision input
  • Camera-Out (Debug/auxiliary feed)
• Audio:
  • Dual PDM microphones – Beamforming-capable
• Environmental:
  • ToF sensor (VL53L5CX or equivalent)
  • 6-axis IMU (Accel + Gyro)

3. Storage

• uSD card slot – For high-capacity logging (video/audio)
• NOR Flash (64Mb+) – Firmware/ML model storage

Advanages

Fast start & Efficient Development with full open-source stack

• Camera integration pipelines
• Communication/transport infrastructure
• Out-of-the-box Android integration

Energy-Efficient Edge AI

• Ultra-low-power inference
• Scalable performance per watt
• Customizable for battery-powered deployments

Flexible Customization for Your Use Case

• Easily swap the neural network for your own model (e.g., ANPR, wildlife recognition)
• Modify firmware, APIs, and business logic as needed
• Customize Android app features and UI; adapt hardware enclosure if needed

Fast Track from Prototype to Product

• Complete open-source stack (code, docs, working examples)
• Short time-to-market and low development cost
• Optional Grovety support for optimized models and integration

Additional links:

Open-source object detection and classification project