Revision as of 06:47, 8 August 2023

Preface

NPU Introduce

RK3568

• Neural network acceleration engine with processing performance up to 0.8 TOPS
• Support integer 4, integer 8, integer 16, float 16, Bfloat 16 and tf32 operation
• Support deep learning frameworks: TensorFlow, Caffe, Tflite, Pytorch, Onnx NN, Android NN, etc.
• One isolated voltage domain to support DVFS

RK3588

• Neural network acceleration engine with processing performance up to 6 TOPS
• Include triple NPU core, and support triple core co-work, dual core co-work, and work independently
• Support integer 4, integer 8, integer 16, float 16, Bfloat 16 and tf32 operation
• Embedded 384KBx3 internal buffer
• Multi-task, multi-scenario in parallel
• Support deep learning frameworks: TensorFlow, Caffe, Tflite, Pytorch, Onnx NN, Android NN, etc.
• One isolated voltage domain to support DVFS

RKNN SDK

RKNN SDK （Password: a887）include two parts:

• rknn-toolkit2
• rknpu2

├── rknn-toolkit2
│   ├── doc
│   ├── examples
│   ├── packages
│   └── rknn_toolkit_lite2
└── rknpu2
    ├── doc
    ├── examples
    └── runtime

rknpu2

'rknpu2' include documents (rknpu2/doc) and examples (rknpu2/examples) to help to fast develop AI applications using rknn model(*.rknn).

Other models (eg:Caffe、TensorFlow etc) can be translated to rknn model through 'rknn-toolkit2'.

RKNN API Library file librknnrt.so and header file rknn_api.h can be found in rknpu2/runtime.

Released BSP and images have already include NPU driver and runtime libraries.

Here is two examples built in released images:

1. rknn_ssd_demo

2. rknn_mobilenet_demo

rknn-toolkit2

TBD