RV1126 Security Cam Board Manufacturers

Rockchip TC-RK3399 system on module(TC-RK3399 Core Board For Stamp Hole) takes Rockchip RK 3399 chip, 64bits, which takes dual system “server class” Cortex -A72 and quad core cortex A53, its dominant frequency 1.8Ghz, and better than other kernel such as A15/A17/A57.

Thinkcore is a leading China RK3588 Golden Finger Development Board manufacturers. Open source and SDK are provided for Rockchip RK3588 development board.Thinkcore Technology Co., Ltd. is a technical enterprise focusing on embedded hardware research and development, design, production and sales.

RK3566 System on module is equipped with Rockchip Quad coretex- A55 processor RK3566. It is suitable for scenarios such as smart NVR, cloud terminal, IoT gateway, industrial control, edge computing, turnstile gate, NAS and vehicle control.

Thinkcore Technology is a leading China Luban Cat RK3568 Single Board Computer Development Board manufacturers. Thinkcore Technology Co., Ltd. is a technical enterprise focusing on embedded hardware research and development, design, production, and sales. We supply high-quality RK3568 single-board computers. We are expecting to become your long-term partner in China.

Thinkcore Technology is a leading China RK3568/RK3568J Golden Finger Development Board manufacturers. Open source and SDK are provided for Rockchip RK3568 /RK3568J development board. Thinkcore Technology Co., Ltd. is a technical enterprise focusing on embedded hardware research and development, design, production and sales.

TC-RK3568 Core board is a high-performance core board designed by Shenzhen Thinkcore based on the RK3568 micro processor of Rockchip. The CPU adopts a quad core 64 bit Cortex-A55, 22nm advanced technology, and the main frequency is up to 2.0GHz; Integrated ARM G52 2EE GPU, fully compatible with OpenGL ES 1.1/2.0/3.2, OpenCL 2.0, Vulkan 1.1, etc; Built in the third generation NPU RKNN developed by Rockchip Micro, the computing power is 0.8Tops, and it supports deep learning frameworks such as caffe, TensorFlow, mxnet, etc. The model can be converted with one click.