AI BriefingGuide to Optimizing BERT Inference on Intel CPUs
Key point
It explains how to optimize BERT model inference performance by leveraging the hardware and software features of Intel Xeon CPUs.
Details
Intel Ice Lake Xeon processors can improve NLP task inference performance by up to 75% compared to previous generations through AVX512 and VNNI (Vector Neural Network Instructions) capabilities.
To maximize performance, core libraries from the oneAPI ecosystem are utilized:
- oneMKL: Provides highly efficient linear algebra routines.
- oneDNN: Optimizes deep neural network operations such as ReLU and Fully-connected.
- oneTBB/OpenMP: High-level parallelization frameworks.
PyTorch and TensorFlow already have MKL and oneDNN built in, providing optimized performance without any additional configuration. When more precise hardware control is needed, custom optimized versions such as Intel PyTorch Extension (IPEX) can be used.
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