Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

作者: JON J K. (Intel) 最后更新时间: 2018/05/30 - 07:00

Intel® Math Kernel Library Improved Small Matrix Performance Using Just-in-Time (JIT) Code Generation for Matrix Multiplication (GEMM)

    The most commonly used and performance-critical Intel® Math Kernel Library (Intel® MKL) functions are the general matrix multiply (GEMM) functions.

作者: Gennady F. (Blackbelt) 最后更新时间: 2019/03/21 - 03:01
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Parallel Universe Magazine - Issue 28, April 2017

作者: 管理 最后更新时间: 2019/09/30 - 16:45

Intel® CPU Excels in MLPerf* Reinforcement Learning Training

Today, MLPerf* consortium, a group of 40 companies and university research institutes, published the 2nd round of the benchmark results based upon ML

作者: Koichi Yamada (Intel) 最后更新时间: 2019/09/30 - 16:50

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