NPU Brief Introduction to Development

NPU Characteristics

Supports 8 bit/16 bit operation with 3.0 TOPS performance

Compared with the large chip scheme of GPU as AI computing unit, the
power consumption of GPU is less than 1%.

Could load Caffe / Mxnet / TensorFlow model Directly

Provide AI development tools: support rapid model transformation,
development board end-to-side conversion API, TensorFlow / TF Lite /
Caffe / ONNX / Darknet and other models

Provide AI application development interface: support Android NN API,
RKNN cross-platform API, Linux support TensorFlow development

Process

The complete NPU development process is shown in the following figure


1. model training

In the model training stage, users choose the appropriate framework
(such as Caffe, TensorFlow, etc.) according to the needs and actual
conditions to train to get the model that meets the needs. The trained
model can also be used directly.

2. Model transformation

In this stage, the model obtained from model training is transformed
into the model available to NPU through RKNN Toolkit.

3. Program Development

The last stage is to implement business logic for Python API
development program based on RKNN API or RKNN Tookit

This document mainly introduces model transformation and RKNN-based
program development, and does not involve the content of model
training.