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RK3566 Core Board
  • RK3566 Core BoardRK3566 Core Board
  • RK3566 Core BoardRK3566 Core Board
  • RK3566 Core BoardRK3566 Core Board
  • RK3566 Core BoardRK3566 Core Board

RK3566 Core Board

RK3566 Core Board TC-RK3566 Highlights 1: High performance CPU & GPU TC-RK3566 Highlights 2: New generation (3rd Gen) Rockchip ISP TC-RK3566 Highlight 3: Powerful multimedia decode/encode capability TC-RK3566 Highlight 4: Integrated efficient RKNN AI processing unit

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Product Description

RK3566 Core Board Features

TC-RK3566 Highlights 1: High performance CPU & GPU

New ARM architecture and advanced process brings higher performance and power efficiency

 

TC-RK3566 Highlights 2: New generation (3rd Gen) Rockchip ISP

up to 8M@30fps processing power, supporting time-sharing and multiplexing dual camera

Powerful HDR function makes the image clear under backlight or strong light conditions

Support dual channel simultaneous zooming output

Noise cancellation function, so that the image under low light conditions is also delicate

Support defogging function, can see clearly even in haze

Support lateral correction of LDCH to remove the distortion caused by the sensor lens


TC-RK3566 Highlight 3: Powerful multimedia decode/encode capability

Support 4KP60 H.264/H.265/VP9 and other formats HD decoding

Support simultaneous decoding of multiple video sources

Support HDR10, excellent performance in color and dynamic range

Support image post-processing, deinterleaving, denoising, color enhancement, resolution increase

Support 1080p 60fps H.264 and H.265 format encoding

Support dynamic bit rate, frame rate, resolution adjustment


TC-RK3566 Highlight 4: Integrated efficient RKNN AI processing unit

NPU with 0.8TOPs computing power

Embedded neural network hardware accelerator, support INT8, INT16, FP16 efficient operation

NPU hardware natively supports technologies such as pre-processing merging, channel quantization, and zero skipping

Support lossless compression of INT8, INT16, FP16 neural network parameters

The NPU core supports ordinary convolution, depth separable convolution, deconvolution, hole convolution, fully connected layer and pooling layer

NPU internal blocks include multiply-add operations, activation, LUT and precision conversion units, and support custom layer construction

Support one-click model conversion, support Caffe/TensorFlow/TF-Lite/ONNX/PyTorch/Keras/Darknet mainstream framework models



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