The research team designed a self-driving bicycle experiment to evaluate the chip’s capability for integrating multimodal information and making prompt decision

The research team designed a self-driving bicycle experiment to evaluate the chip’s capability for integrating multimodal information and making prompt decisions. Equipped with the Tianjic chip and IMU sensor and a camera the bicycle was tasked with performing functions such as real-time object detection, tracking, voice-command recognition, riding over a speed bump, obstacle avoidance, balance control and decision making. The research team developed a variety of neural networks (CNN, CANN, SNN and MLP networks) to enable each task. The models were pretrained and programmed onto the Tianjic chip, which can process the models in parallel and enable seamless on-chip communication across different models. Video of the unmanned bike: https://youtu.be/VjSs6KCLTC0 https://youtu.be/VjSs6KCLTC0