…oneAPI Collective Communications Library (oneCCL), Intel-optimized NumPy, Intel® Math Kernel Library, and Intel® Threading Building Blocks has ensured we get the best accelerated performance on CPU and GPU devices. We have…
…Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software…
…convolutional image-based classification models optimized for the Intel® processors used in GE Healthcare imaging systems. The move to the Intel® Distribution of OpenVINO™ toolkit improved performance across all models, with the…
…Optimizing simulation performance and therefore reducing the runtime pays off twice for the customer: First, the reduced runtime saves costs on cloud hardware, and second, it simply saves time. Challenge: Hardware Bottlenecks…
…The OpenVINO toolkit is open source and used to optimize and deploy AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing, and other common tasks. Use…
…How to Perform Static Quantization and Dynamic Quantization The Intel extension extends PyTorch with up-to-date features and optimizations for an extra performance boost on Intel hardware. Installation Instructions for Intel…
…Across your entire IT landscape, you can take advantage of the Intel® portfolio to support your AI workloads with an optimal blend of performance and cost efficiency. In fact, many of your…
…The resulting proof-of-concept analytic pipeline, running on Dell PowerEdge Servers with Intel data center CPUs, showed stunning performance for processing genomic data. 1 Leveraging optimizations for Intel® x86 architecture, the…
…Optimizing TensorFlow Model Inference TensorFlow is one of the most popular deep learning frameworks and improving the inference performance of your TensorFlow model is an important part of optimizing your AI workflow…
…The Model Optimizer performs analyses and adapts the models for optimal execution on different end devices. This way, algorithms can be deployed to various Intel hardware platforms, saving time and development resources…