Speed Up Inference Deployment without Sacrificing Accuracy
Deploy Low-Precision Inference Solutions on Popular Frameworks
Deep neural networks (DNNs) show state-of-the-art accuracy in a wide range of computation tasks. However, they still face challenges during application deployment due to their high computational complexity of inference. Low precision is one of the key techniques that help conquer the problem.
Intel® Low Precision Optimization Tool (Intel® LPOT) is an open-source Python* library designed to help you quickly deploy low-precision inference solutions on popular deep-learning frameworks such as TensorFlow*, PyTorch*, MXNet*, and ONNX* (Open Neural Network Exchange) runtime. The tool automatically optimizes low-precision recipes for deep-learning models to achieve optimal product objectives, such as inference performance and memory usage, with expected accuracy criteria.
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