Banana Pi BPI-M5 Pro open source single board computer pcb module

Main Product Parameters:

●SoC: Rockchip RK3576

●CPU: Integrated with four Cortex-A72 cores @ 2.2GHz and four Cortex-A53 cores @ 1.8GHz, along with a separate NEON co-processor.

●GPU: ARM Mali G52 MC3 GPU

●NPU: Up to 6 TOPs computing power (INT8), supports INT4/INT8/INT16 mixed operations.

●VPU/Encoding & Decoding

●RAM: 8/16GB 32-bit LPDDR4x, default is 8GB. RK3576 supports a maximum of 16GB.

●Flash: 32/64/128GB eMMC, default is 64GB eMMC.

Product Description

BananaPi BPI‑M5Pro pcb module is a high-performance AIoT and edge computing single board computer based on Rockchip RK3576. It inherits the open spirit and excellent scalability of BananaPi, supports 8K video encoding and decoding, provides dual Gigabit network ports, WiFi 6 & BT5, multiple video outputs and other rich interfaces. It is compatible with multiple operating systems and is suitable for ARM-based PCs, edge computing devices, personal mobile Internet devices and other digital multimedia applications.

Key Parameter：

* SoC: Rockchip RK3576
* CPU: Integrated with four Cortex-A72 cores @ 2.2GHz and four Cortex-A53 cores @ 1.8GHz, along with a separate NEON co-processor.
* GPU: ARM Mali G52 MC3 GPU
* NPU: Up to 6 TOPs computing power (INT8), supports INT4/INT8/INT16 mixed operations.
* VPU/Encoding & Decoding:
* Hardware Decoding: Supports H.264, H.265, VP9, AV1, and AVS2 up to 8K@30fps or 4K@120fps.
* Hardware Encoding: Supports H.264 and H.265 up to 4K@60fps, high-quality JPEG encoder/decoder supports up to 4K@60fps.
* RAM: 8/16GB 32-bit LPDDR4x, default is 16GB. RK3576 supports a maximum of 16GB.
* Flash: 32/128GB eMMC, default is 128GB eMMC.
* Operating Systems:Official Rockchip Support: Android 14, Debian 11, Buildroot;Third-party Support: Armbian

Product Features:

1. Computing performance
CPU+NPU collaboration: A72 large core is responsible for general computing, A53 small core handles low-power tasks, NPU accelerates AI reasoning, and energy efficiency is improved by 30% in typical scenarios.
GPU acceleration: Mali-G52 MC3 supports OpenGL ES 3.2 and Vulkan 1.1, and can run lightweight 3D applications smoothly.

2. Multimedia processing capabilities
8K video decoding: supports mainstream encoding formats, suitable for digital signage, video surveillance and other scenarios.
Multi-screen output: HDMI+DP+MIPI DSI combination can drive three-screen display and adapt to complex interactive interfaces.

3. Scalability and compatibility
Storage expansion: M.2 PCIe slot supports NVMe SSD, with a read and write speed of up to 1.5GB/s.
System compatibility: Official support for Android 14, Debian 11, Buildroot, third parties provide Armbian and other images, developers can choose flexibly.

Application Scenarios:

1. Edge computing device
As a lightweight server, deploy AI models for local reasoning (such as face recognition and target detection) to reduce cloud dependence.
Applicable to scenarios such as smart retail and industrial quality inspection to achieve real-time data processing and decision-making.

2. Smart home hub
Integrates NAS, gateway, and development board functions to build a home cloud storage and device control center.
Through dual Gigabit network ports and WiFi 6, high-speed interconnection of multiple devices is achieved, and Zigbee/Z-Wave protocol expansion is supported.

3. Industrial automation
Connect PLC, sensors and other devices, and realize production line monitoring and automatic control through GPIO interface.
Wide temperature design and anti-interference ability, adapt to harsh industrial environments.

4. Multimedia terminal
Digital signage: 8K decoding + multi-screen output to create a high-definition advertising display system.
Video conferencing: Support 4K encoding and multi-channel camera access to enhance remote collaboration experience.

Summary

Banana Pi BPI-M5 Pro has become an ideal choice for edge computing, smart home, industrial control and other fields with its powerful RK3576 chip, rich interface expansion and AI acceleration capabilities. Whether developers are conducting prototype verification or enterprises are deploying lightweight AI applications, efficient development can be achieved through its flexible configuration and mature ecosystem.