Multi-grained system integration for hybrid-paradigm brain-inspired computing

Hybrid neuromorphic computing supporting the prevailing artificial neural networks and neuroscience-inspired models/algorithms offers substantial flexibility for cross-paradigm model integration. It is one of the most promising technologies for accelerating intelligence development, ultimately contributing to artificial general intelligence development. Recently, an increasing number of hybrid neuromorphic computing chips have been reported, but such research focuses on chip design without demonstrating systems for large-scale workloads. To this end, we construct a multi-grained system based on many Tianjic chips, presenting a large-scale system for hybrid-paradigm brain-inspired computing. With different numbers of chips and different connection topologies, we develop a Tianjic card and a Tianjic board as the infrastructure for building embedded systems and cloud servers, respectively. Extensive measurements of the communication latency, computational latency, and power consumption evidence the superior potential of Tianjic systems for exploring brain-inspired computing for artificial general intelligence.