Tengine is a lite, high performance, modular inference engine for embedded device

Efficient Inference of Transformer models

Free TPU for FPGA with compiler supporting Pytorch/Caffe/Darknet/NCNN. An AI processor for using Xilinx FPGA to solve image classification, detection, and segmentation problem.

Samples code for world class Artificial Intelligence SoCs for computer vision applications.

TinyVision - A Tiny Linux Board / IPC / Server / Router / And so on...

The Pipeline example based on AXear-Pi (AX620A) , AXera-Pi Pro (AX650N) and AXera-Pi Zero (AX620Q) shows the software development skills of ISP, Image Processing, NPU, Encoding, and Display modules, which is helpful for users to develop their own multimedia applications.

FREE TPU V3plus for FPGA is the free version of a commercial AI processor (EEP-TPU) for Deep Learning EDGE Inference

YoloV5 NPU for the RK3566/68/88

Advanced driver-assistance system with Google Coral Edge TPU Dev Board / USB Accelerator, Intel Movidius NCS (neural compute stick), Myriad 2/X VPU, Gyrfalcon 2801 Neural Accelerator, NVIDIA Jetson Nano and Khadas VIM3

hardware design of universal NPU(CNN accelerator) for various convolution neural network

Run your yolov7 object detection with Rockchip NPU platforms (RK3566, RK3568, RK3588, RK3588S, RV1103, RV1106, RK3562).

Small Heterogeneous & AI Powered Computing SBC Based on V853

Easy usage of Rockchip's NPUs found in RK3588 and similar chips

ROS 2 Inference sample for using Rockchip NPU.

Easier usage of LLMs in Rockchip's NPU on SBCs like Orange Pi 5 and Radxa Rock 5 series

Convert and run scikit-learn MLPs on Rockchip NPU.

Superresolution running on Rockchip NPU (RK3588, etc..)

YoloV8 segmentation NPU for the RK 3566/68/88

NPUsim: Full-system, Cycle-accurate, Value-aware NPU Simulator

Rock Pi 5 image with Ubuntu 22, OpenCV, deep learning frameworks and NPU drivers