Accelerate Edge AI and multimedia projects with the FireBeetle 2 ESP32-P4. This compact, highly-integrated platform combines a 360MHz dual-core RISC-V CPU with native MIPI-CSI/DSI interfaces for high-resolution video, an onboard microphone, and Wi-Fi 6 connectivity. It's engineered to be the ideal core for building AI cameras, interactive GUIs, and smart home panels, providing a powerful yet streamlined foundation to move quickly from concept to high-performance prototype.
Features of ESP32-P4
High-Performance AI Core
At its center, the board harnesses a 360MHz dual-core RISC-V processor with single-precision FPU and AI instruction extensions. This provides the necessary horsepower for demanding machine learning tasks, allowing for the direct deployment of lightweight models like TensorFlow Lite for applications such as facial recognition, object detection, and voice-based AI assistants.
Native Multimedia Powerhouse
The board excels at multimedia processing thanks to its native MIPI-CSI and MIPI-DSI interfaces, which allow for the direct connection of high-resolution cameras and displays, eliminating the need for complex adapter boards. With onboard hardware accelerators for H.264 video and JPEG encoding, it can efficiently handle video streams up to 1080p, making it the perfect core for AI cameras and interactive GUIs. Furthermore, the interfaces are pin-compatible with Raspberry Pi peripherals, greatly simplifying component sourcing.
Highly Integrated, Compact Design
Beyond its processing power, the FireBeetle 2's value lies in its thoughtful integration within a minimal 25.4 x 60mm footprint. It features a built-in PDM digital microphone for voice-activated applications and a TF card slot for expansive local data storage. This high level of integration reduces the need for external modules, simplifies the overall product design, and is ideal for creating compact, space-constrained devices.
Future-Proof Connectivity
Equipped with an onboard ESP32-C6 co-processor, the FireBeetle 2 supports the latest Wi-Fi 6 standard. This provides higher throughput, lower latency, and improved performance in congested network environments—critical for data-intensive AIoT applications. This is complemented by stable Bluetooth 5 (LE) for versatile peripheral communication.
Board Layout and Pinout Diagram of FireBeetle 2 ESP32-P4
The diagram above highlights the key components and interfaces of the FireBeetle 2 ESP32-P4. Refer to the list below for detailed descriptions of the main features:
- MIPI-DSI Interface: 2-lane MIPI Display port, pin-compatible with Raspberry Pi displays for easy sourcing and setup.
- MIPI-CSI Interface: 2-lane MIPI Camera port, pin-compatible with Raspberry Pi cameras, ideal for AI vision projects.
- ESP32-C6 Co-processor: Provides powerful and modern Wi-Fi 6 and Bluetooth 5 (LE) connectivity, connected to the main P4 chip via SDIO.
- High-Speed USB OTG (Type-C): A USB 2.0 High-Speed port for fast data transfer, supporting OTG to connect to other USB devices.
- USB CDC & Power (Type-C): The primary port for programming, debugging (via serial), and powering the board.
- PDM Microphone: An onboard digital microphone, allowing for direct development of voice recognition and other audio-based AI applications.
- TF Card Slot: A MicroSD card slot for storing AI models, images, and data logs.
- BOOT & RST Buttons: Standard buttons for resetting the board and entering the firmware download mode.
For a complete, pin-by-pin functional description, please refer to the Product Wiki.
Looking for an easier start?
For rapid prototyping without breadboards or soldering, consider the FireBeetle 2 ESP32-P4 Development Kit. It includes this board plus our dedicated IO Expansion Board, providing multiple plug-and-play ports for all your peripherals.
Compatibility Notice: This board features the ESP32-P4 chip (v1.0) running at 360MHz. You can use it with both Arduino IDE and ESP-IDF. Please note that two specific advanced features, MIPI-CSI and AI functions, currently require ESP-IDF. All other functions are fully compatible with the Arduino IDE.
