Deep learning (DL) has seen phenomenal growth in the last few years and is being applied to solve problems in a variety of areas such as image classification, speech recognition, and object detection. Deep learning frameworks like TensorFlow, Caffe, MXNet, and PyTorch allow data scientists to implement neural network models to solve various problems. Intel engineers and framework owners have optimized these frameworks to improve their performance on Intel Xeon processor-based platforms. Huma Abidi details these collaborative optimization efforts and explains how deep learning framework users can leverage these optimizations. Along the way, Huma provides specific tuning tips to get the best performance on Intel Xeon processors.
为现代英特尔 CPU 优化深度学习框架
深度学习（Deep Learning，DL）在近几年取得了显著的发展，并在多个领域（例如图像识别、语音识别和目标检测）被用于解决问题。诸如TensorFlow、Caffe、MXNet和PyTorch这样的框架可以让用户（比如数据科学家）在多种问题上运用神经网络。Intel工程师与框架所有者已经合作，优化这些框架以提高它们在基于Intel® Xeon®处理器平台上的性能。本议题中我们会讨论这些合作优化工作及描述深度学习框架用户如何使用这些优化。还会提供一些在Intel® Xeon®处理器上得到最佳性能的特殊技巧。
Huma Abidi is the engineering director of the Artificial Intelligence Product Group at Intel, where she is responsible for deep learning framework software optimization for Intel Xeon processors. Huma joined Intel as software engineer and has since worked in a variety of engineering, validation, and management roles in the area of compilers, binary translation, and machine learning and deep learning. She received the Intel Achievement Award for her work in the Software and Services Group and was twice recognized with the Intel Software Quality award. She is passionate about women’s education and serves on the board of directors at ROSHNI, a philanthropic organization that educates and supports underprivileged girls in India. Huma holds a BS in pre-med and chemistry and an MS in computer science from the University of Massachusetts.
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