Banana Pi BPI-SM9 Brings BM1688 AI Performance to Edge Vision Systems

Banana Pi BPI-SM9

The Banana Pi BPI-SM9 appears as the company’s first compute module built around a dedicated AI and vision processor, moving beyond its regular SBC lineup. It focuses on edge-side intelligent computing and supports workloads such as video analytics, computer vision, AI inference, and multimedia tasks in a compact, power-efficient design.

As demand grows for embedded and edge-AI platforms, the BPI-SM9 aims to deliver a balanced mix of performance and flexibility. Developers working on real-time video, object detection, or any AI-driven project can use its combination of a general-purpose CPU, a strong NPU, and a wide set of I/O interfaces to build reliable and fast edge systems.

Previously, we covered the Banana Pi BPI-CM6 RISC-V compute module, which features the SpacemiT K1 octa-core AI SoC and supports an optional I/O board for expansion. It is an industrial-grade module designed for high-performance edge computing and embedded applications.

Banana Pi BPI-SM9 top and bottom

Banana Pi BPI-SM9 Compute Module Specifications:

• SoC/AI Processor: SOPHGO BM1688 with 8x Arm Cortex-A53 cores @ 1.6 GHz and 16 TOPS NPU (INT8), supporting INT4/INT8/FP16/BF16/FP32
• Memory: 8GB LPDDR4X
• Storage: 32GB eMMC flash; SD/SDIO support
• Video:
  • Video Decoder: Up to 16× 1080p60 or multi-format decode up to 8K/4K/1080p/720p
  • Video Encoder: Up to 10× 1080p30 H.264/H.265
• Display Output: HDMI 2.0 up to 8K
• Camera Interfaces: Dual MIPI-CSI (up to 8MP ×2)
• ISP Features: HDR, 3DNR, LDC, dehaze, stereo depth, image stitching, fisheye correction
• Connectivity: Dual Gigabit Ethernet (through carrier), USB 3.0, USB 2.0
• Expansion: PCIe 3.0 interface, SATA, multiple UART, I²C, SPI, PWM, ADC, GPIO
• Power: 12V input (via carrier board)
• Form Factor: 260-pin SO-DIMM module, 100 x 79 x 16 mm
• Operating Temperature: –20°C to +70°C

Banana-Pi-BPI-SM9 Dimensions

The BPI-SM9 supports widely used deep-learning frameworks and model formats, allowing developers to run ONNX, Caffe, or TFLite models using TensorFlow, PyTorch, Paddle, or BM1684/BM1684X-compatible toolchains. This reduces integration effort and helps teams move from development to deployment without additional conversion steps. Its hardware-accelerated ISP and multimedia engines further improve performance by offloading image and video processing from the CPU, enabling real-time analytics and multi-channel vision workloads to run efficiently.

BPI-SM9 developer’s kit

The Banana Pi BPI-SM9 AI compute module is still new in the market, and availability remains limited. Many sellers list it as pre-order or out of stock, and official pricing has not been standardized yet. Local stock is also scarce, so most buyers may need to rely on international sellers until wider distribution becomes available.

Images used courtesy of banana-pi