PyTorch acceleration baked into the latest generation of Intel Xeons.
That will help speed up the 200 trillion predictions and 6 billion translations Facebook does every day. https://t.co/2gM75pFvrC
— Yann LeCun (@ylecun) April 9, 2019
Intel® Deep Learning Boost (Intel® DL Boost) is a group of acceleration features introduced in our 2nd Generation Intel® Xeon® Scalable processors. It provides significant performance increases to inference applications built using leading deep learning frameworks such as PyTorch*, TensorFlow*, MXNet*, PaddlePaddle*, and Caffe*. 
Intel DL Boost follows a long history of Intel adding acceleration features to its hardware to increase the performance of targeted workloads. The initial Intel Xeon Scalable processors included a 512-bit-wide Fused Multiply Add (FMA) instruction in the Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instructions it introduced. This FMA instruction facilitated increases in data parallelism and helped Intel Xeon Scalable processors deliver 5.7x performance for AI and deep learning applications. 
With Intel DL Boost, we build upon this foundation to further accelerate AI on Intel® architecture. The first of several innovations planned for Intel DL Boost are the Vector Neural Network Instructions (VNNI), which have two main benefits to deep learning applications:
Intel DL Boost helps contribute to a theoretical peak speedup of 4x for INT8 inference on 2nd Gen Intel Xeon Scalable processors,  in comparison to FP32 inference. Further, with 56 cores per socket, we are predicting that Intel Xeon Platinum 9200 processors will deliver up to twice the performance of Intel Xeon Platinum 8200 processors.  In fact, Intel Xeon Platinum 9282 processors recently demonstrated the ability to exceed the performance of NVIDIA* Tesla* V100 on ResNet-50 inference.
You can learn more about the technical details of VNNI from our recent Intel.ai blog on the topic.
The rapid proliferation of AI inference services, the need for these services to render results quickly, and the tendency for increasingly complex deep learning applications to be processor-intensive are helping drive unprecedented demand for high-performance, low-latency compute. It is often easiest and most efficient to meet this demand with IT infrastructure already in place or already familiar – the Intel Xeon Scalable processor-based systems trusted for so many other workloads. Fortunately, as customers and researchers have shown time and again, Intel architecture makes a highly performant platform for AI inference.
With Intel DL Boost, Intel architecture and 2nd Gen Intel Xeon Scalable processors are a more capable AI inference platform than ever before. Even better, Intel DL Boost’s innovation will continue in the next generation of Intel Xeon Scalable processors, in which we will introduce support for the bfloat16 floating-point format. Look for more information on this soon.
We have been working with the AI community to optimize the most popular open source deep learning frameworks for Intel DL Boost to help developers benefit from the performance and efficiency gains it provides.
Developers can use tools Intel offers to convert a FP32 trained model to an INT8 quantized model. This new INT8 model will benefit from Intel DL Boost acceleration when used for inference in place of the earlier FP32 model and run on 2nd Gen Intel Xeon Scalable processors. As additional support, Intel also provides a Model Zoo, which includes INT8 quantized versions of many pre-trained models, such as ResNet101, Faster-RCNN, and Wide&Deep. We hope these models and tools get you up and running with Intel DL Boost more quickly.
Several earlier intel.ai blogs provide more details on Intel DL Boost integrations into various popular deep learning frameworks and results customers are seeing from early use of these optimizations.
TensorFlow: Our blog on accelerating TensorFlow inference explains how to use the Intel AI Quantization Tools for TensorFlow to convert a pre-trained FP32 model to a quantized INT8 model. Several pre-trained INT8 quantized models for TensorFlow are included in the Intel Model Zoo in categories like image recognition, object detection, and recommendation systems. Dell EMC has reported a greater than 3x improvement in performance over the initial Intel Xeon Scalable processors using our pretrained INT8 Resnet50 Model and 2nd Gen Intel Xeon Scalable processors with Intel DL Boost. 
PyTorch: Intel and Facebook have partnered to increase PyTorch performance with Intel DL Boost and other optimizations. With Intel DL Boost and 2nd Gen Intel Xeon Scalable processors, we have found up to 7.7x performance for a FP32 model and up to 19.5x performance for an INT8 model when running ResNet50 inference.  As a result of this collaboration, Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) optimizations are integrated directly into the PyTorch framework, enabling optimization of PyTorch models with minimal code changes. We’ve provided a blog that should be a good way to get started with PyTorch on 2nd Gen Intel Xeon Scalable processors.
Apache MXNet: The Apache MXNet community has delivered quantization approaches to enable INT8 inference and use of VNNI. We have found 3.0x performance for ResNet-50 for an INT8 model with Intel DL Boost and 2nd Gen Intel Xeon Scalable processors in comparison to a FP32 on Intel Xeon Scalable processors.  iFLYTEK, which is leveraging 2nd Gen Intel Xeon Scalable processor and Intel® Optane™ SSDs for its AI applications, has reported that Intel DL Boost has resulted in similar or better performance in comparison to inference using alternative architectures.
PaddlePaddle: Intel and Baidu have collaborated since 2016 to optimize PaddlePaddle performance for Intel architecture. We’ve provided an in-depth overview of INT8 support in PaddlePaddle. In Intel’s testing, INT8 inference resulted in 2.8x throughput for ResNet-50 v1.5 with just 0.4% accuracy loss in comparison to an earlier FP32 model. 
Intel Caffe: Our Intel Caffe GitHub Wiki explains how to use low-precision inference to speed up performance without losing accuracy by using our Calibrator accuracy tool. JD.com collaborated with Intel engineers to use Intel DL Boost to increase the performance of a text detection application by 2.4x with no accuracy degradation in comparison to an earlier FP32 model. 
With all-new software libraries and optimizations, coupled with hardware innovation, CPUs have never been more performant for AI than they are today. My team and I look forward to continuing to deliver these AI breakthroughs on Intel architecture. To follow our work, please stay tuned to intel.ai and follow Intel AI on Twitter at @IntelAI and @IntelAIResearch.
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Intel® Advanced Vector Extensions (Intel® AVX)* provides higher throughput to certain processor operations. Due to varying processor power characteristics, utilizing AVX instructions may cause a) some parts to operate at less than the rated frequency and b) some parts with Intel® Turbo Boost Technology 2.0 to not achieve any or maximum turbo frequencies. Performance varies depending on hardware, software, and system configuration and you can learn more at http://www.intel.com/go/turbo.
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