AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs.
The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer models and Mixture of Experts through dedicated engines and sparse computation acceleration.
Its broader scope includes multi-dimensional distributed parallelism for large-scale model training, high-performance inference optimization via quantization and pruning, and advanced memory management techniques such as tiled memory and unified memory spaces. It also addresses hardware interconnects and collective communication primitives to scale compute clusters.
The project is primarily implemented and documented via Jupyter Notebooks.