There are two main approaches to developing artificial general intelligence. One is rooted in neuroscience, and attempts to construct circuits that closely mimic the brain. The other is grounded in computer science, and uses computers to execute machine-learning algorithms.
In this week’s issue, Luping Shi and his colleagues reveal the Tianjic chip — an electronic chip that integrates the two approaches into one hybrid platform. The Tianjic chip has multiple functional cores that are readily reconfigurable, enabling it to accommodate both machine-learning algorithms and brain-inspired circuits.
A key innovation from the research team is Tianjic’s unified function core (FCore) which combines essential building blocks for both artificial neural networks and biologically networks — axon, synapse, dendrite and soma blocks.
研究人员将芯片安装到无人自行车，来证明该芯片的强大功能和潜力。这款无人自行车能自我平衡，具有声控功能，可探测并避开障碍物。这都是“天机”芯片能同时处理多种算法和模型的强大功能。The researchers demonstrate the potential of this approach by incorporating one of their chips into a riderless autonomous bicycle, which can self-balance, is voice controllable and can detect and avoid obstacles, all as a result of the Tianjic chip’s simultaneous processing of versatile algorithms and models.
Equipped with the Tianjic chip and IMU sensor, a camera, steering motor, driving motor, speed motor and battery, 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.
The research team told Chinese media they expect the Tianjic chip to be deployed in autonomous vehicles and smart robots. They have already started research on the next-generation chips and expect to close the R&D stage early next year.