Shaolin Pi Development Board
Shaolin Pi development board is a high-performance platform based on BM1684, providing around 20 TOPS of computing power. With BM1684 as its core component, the board features a fully autonomous and controllable processor, delivering exceptional computing power and multi-channel video encoding and decoding capabilities. It supports 3 mini-PCIe and 4 USB interfaces, allowing for the expansion of various peripheral modules. It optimizes configurations according to scene requirements, balancing cost-effectiveness, energy efficiency, and functionality. The hardware ecosystem is extensive, accommodating diverse peripheral connections. It boasts support for a rich software development ecosystem, compatible with mainstream deep learning frameworks. The 'Shaolin Pi' core board can expand connectivity to screens, keyboards, mice, cameras, headphones, VR, and other devices. It enables the creation of an all-encompassing edge computing workstation for various AI experiments. Additionally, it can be embedded into unmanned vehicles and drones, enabling edge computing for mobile terminals. Leveraging the Shaolin Pi development board, our company has developed the KT001 smart car and S550 deep learning drone. Participants can also request the deep learning car as a hardware platform to complete their own parameterized works for the competition.
SophonSDK is a deep learning SDK tailored by SOPHGO for its independently developed AI processor. It encompasses capabilities such as model optimization and efficient runtime support required during the neural network inference phase, providing an easy-to-use and efficient end-to-end solution for deep learning application development and deployment.
SophonSDK consists of a Compiler and Library: The Compiler is responsible for offline compilation and optimization of neural network models trained under third-party deep learning frameworks, generating the required BModel for the final runtime. It currently supports Caffe, Darknet, MXNet, ONNX, PyTorch, PaddlePaddle, TensorFlow, among others. The Library includes BM-OpenCV, BM-FFmpeg, BMCV, TPURuntime, BMLib, and other libraries, which drive hardware components like VPP, VPU, JPU, TPU, enabling video/image encoding/decoding, image processing, tensor operations, model inference, and other functions for users in deep learning application development.
Documentation Center: https://developer.sophgo.com/site/index/material/30/all.html
Video Tutorials: https://developer.sophgo.com/site/index/course/all/all.html
Development Guide: https://sophgo-doc.gitbook.io/sophonsdk3