- Six-core 64-bit processor, superior general-purpose computing power
- Dual-core ARM Cortex-A72 MPcore processor and quad-core ARM Cortex-A53 MPcore processor are high-performance, low-power and cache application processors.
- Two CPU clusters. Big cluster with dual-coreCortex-A72 is optimized for high-performance and little cluster with quad-core Cortex-A53 is optimized for low power
- Full implementation of the ARM architecture v8-A instruction set, ARM Neon Advanced SIMD (single instruction, multiple data) support for accelerating media and signal processing
- Supporting 8 bit/16 bit operation, AI computing power up to 3.0 TOPs (INT8 Inference)
- Full load calculation is strong and light load operation power consumption is low.
- Compatible with Caffe/Mxnet/TensorFlow model, it can support multiple frameworks, support mainstream layer types, and add custom layer easily
- Provide easy-to-use development tools, PC can complete model conversion, performance prediction, accuracy verification.
- Provide AI application development interface: support Android NN API, RKNN cross-platform API, Linux support TensorFlow development;
- Powerful Multimedia Processing Performance
- Integrated quad-core ARM Mali-T860MP4 GPU, support OpenGL ES1.1/2.0/3.0, OpenCL1.2, Directx11.1, etc., with more bandwidth compression technology
- Strong hardware codec capability
- Multiple video input and output interfaces
- Rich expansion interface
- High-speed on-board connector for more stability and reliability
- Support for multiple operating systems
- Hardware related information
- Software related information
Basic Parameters | |
SoC | Rockchip RK3399Pro |
GPU |
ARM® Mali-T860 MP4 Quad-core GPU Support OpenGL ES1.1/2.0/3.0/3.1, OpenVG1.1, OpenCL, DX11 Support AFBC (frame buffer compression) |
CPU |
Dual-core Cortex-A72 up to 1.8GHz Quad-core Cortex-A53 up to 1.4GHz |
NPU |
Support 8bit/16bit operation, computing power up to 3.0TOPS Full load computing power, low load operation power consumption is low Compatible with Caffe/Mxnet/TensorFlow model, support multiclass framework, support mainstream layer type, easy to add a custom layer Provides easy-to-use development tools, PC-based model conversion, performance estimation, and accuracy verification Provide AI application development interface: support Android NN API, provide RKNN cross-platform API, Linux support TensorFlow development; |
VPU |
Support 4K VP9 and 4K 10bits H265/H264 video decoding, up to 60fps 1080P multi-format video decoding (WMV, MPEG-1/2/4, VP8) 1080P video encoding, support H.264, VP8 format Video post processor: de-interlacing, denoising, edge/detail/color optimization |
RAM |
3GB LPDRR3(CPU 2GB + NPU 1GB) |
Flash |
16GB eMMC |
Hardware Characteristics | |
Ethernet | Built-in Gigabit Ethernet PHY chip, 10/100/1000Mbps adaptive |
Camera Interface |
MIPI-CSI×2,Dual camera interface (built-in dual hardware ISP, up to single 13Mpixel or dual 8Mpixel) |
Display Interface |
MIPI-DSI eDP DP HDMI (Support 480p/480i/576p/576i/720p/1080p/1080i/4k, support RGB format) |
Audio Port |
I2S0:Support user extended use
I2S1:Speaker×1, Headphone×1, MIC×1 |
Type-C | USB3.0/DisplayPort 1.2,OTG |
USB |
USB3.0×1 (according to RK3399Pro design, NPU needs to be mounted on USB3.0, so USB3.0 needs to connect back to NPU, if you need to expand USB3.0 interface, you need external HUB); |
Extension Port |
SDMMC(TF Card)×1; |
Power input | DC 5V |
96Boards BeiQi RK3399Pro AIoT Compute SoM
- Brand: 96 Boards
- Product Code:Seeed-96Boards-BeiQi-RK3399Pro
- Reward Points:159
- Availability:Out Of Stock
-
रo 15,864.00
- Price in reward points:15864
-
- 100 or more रo 15,997.00