Intel® Distribution for Python* versus Non-Optimized Python: Breast Cancer Classification

This case study compares the performance of Intel® Distribution for Python* to that of non-optimized Python using a breast cancer classification. This comparison was done using machine learning algorithms from the scikit-learn* package in Python.
作者: 管理 最后更新时间: 2018/12/12 - 18:00

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
作者: 最后更新时间: 2019/10/15 - 15:30

Migrating Applications from Knights Corner to Knights Landing Self-Boot Platforms

While there are many different programming models for the Intel® Xeon Phi™ coprocessor (code-named Knights Corner (KNC)), this paper lists the more prevalent KNC programming models and further discusses some of the necessary changes to port and optimize KNC models for the Intel® Xeon Phi™ processor x200 self-boot platform.
作者: Michael Greenfield (Intel) 最后更新时间: 2019/10/15 - 16:40

Code Sample: Optimizing Binarized Neural Networks on Intel® Xeon® Scalable Processors

In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.
作者: Yash Akhauri 最后更新时间: 2019/10/15 - 16:50