2 रिपॉजिटरी
Specialized device-side kernels written for non-GPU accelerators like NPUs or MLUs.
Distinct from GPU Kernel Programming: Focuses specifically on non-GPU accelerator programming (MLU/NPU) rather than general graphics hardware kernels.
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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 mo
Writes device-side kernels using C++ or Python to manage task partitioning and synchronization.
This project is a comprehensive educational resource and curriculum focused on the design and implementation of the full machine learning software and hardware stack. It serves as a technical reference for architecting machine learning systems, spanning from low-level programming interfaces to large-scale deployment infrastructure. The project provides instructional guidance on several specialized domains, including the development of AI compilers through intermediate representations and graph optimizations. It covers the architectural patterns required for distributed training across GPU clu
Teaches the implementation of high-performance kernels for specialized AI accelerators and NPUs.