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oneDNN is a library for deep learning acceleration that provides optimized building blocks for neural network training and inference. It manages tensor computation across CPU and GPU hardware, enabling the execution of high-performance primitives for model training and neural network inference optimization. The project distinguishes itself through hardware-specific kernel optimization and the use of just-in-time compilation to target specific processor instruction sets. It supports quantized neural network execution using both static and dynamic quantization to reduce memory usage and increas

oneDNN is a deep learning primitive library and hardware acceleration framework designed to optimize neural network operations. It serves as an inference engine that accelerates the training and execution of computational graphs using optimized primitives for convolutions and matrix multiplications, following the oneAPI standard for cross-architecture performance. The project enables cross-architecture AI deployment by tuning workloads for specific CPU and GPU microarchitectures across different hardware vendors. It integrates with hardware runtimes and system drivers to share execution conte