The World Is Your Lab

Scale from model to reality with the new 2nd Generation Intel® Xeon® Scalable processors—now enhanced for AI and the most popular AI software frameworks.

Empowering AI Service Delivery with an Edge-to-Cloud Intel Technology Portfolio

We recently announced several forward-thinking AI deployments delivering rapid, real-world results for a seamless user experience by drawing on the latest additions to Intel’s diverse silicon portfolio, such as 2nd Generation Intel® Xeon® Scalable processors and Intel® Optane™ DC persistent memory.

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TensorFlow* Optimizations for the Intel® Xeon® Scalable Processor

TensorFlow* is one of the leading deep learning and machine learning frameworks today. Intel worked with Google to incorporate optimizations for Intel® Xeon® and Xeon Phi™ processor based platforms using Intel® Math Kernel Libraries (Intel® MKL). These optimizations resulted in orders of magnitude improvement in performance – up to 70x higher performance for training and up to 85x higher performance for inference.

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AI Developer Webinar Series


Covering the latest in frameworks, optimization tools, and new product launches through the year

iFLYTEK looks to boost their optimized Cloud for artificial intelligence based on 2nd Generation Intel® Xeon® Scalable processors

Intel’s AI technology portfolio includes multi-purpose, purpose-built and customizable hardware platforms, takes both hardware support and software optimization into account...

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Siemens Healthineers Accelerates AI for Cardiology, Powered by 2nd Generation Intel® Xeon® Scalable Processors with Intel® Deep Learning Boost

Siemens Healthineers and Intel are working to accelerate AI for cardiacimaging using 2nd Generation Intel® Xeon® Scalable processors with Intel® DeepLearning Boost...

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Second Generation Intel® Xeon® Processor Scalable Family Technical Overview

New features and enhancements available in the second generation Intel® Xeon® processor Scalable family and how developers can take advantage of them...

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Exploring the path from AI theory to real business value

Find out how businesses can navigate the challenges and opportunities of evolving their AI capabilities, and how Intel® AI Technology can help...

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14x inference throughput improvement on Intel® Xeon® Platinum 8280 processor with Intel® DL Boost: Tested by Intel as of 2/20/2019. 2 socket Intel® Xeon® Platinum 8280 Processor, 28 cores HT On Turbo ON Total Memory 384 GB (12 slots/ 32GB/ 2933 MHz), BIOS: SE5C620.86B.0D.01.0271.120720180605 (ucode: 0x200004d), Ubuntu 18.04.1 LTS, kernel 4.15.0-45-generic, SSD 1x sda INTEL SSDSC2BA80 SSD 745.2GB, nvme1n1 INTEL SSDPE2KX040T7 SSD 3.7TB, Deep Learning Framework: Intel® Optimization for Caffe version: 1.1.3 (commit hash: 7010334f159da247db3fe3a9d96a3116ca06b09a) , ICC version 18.0.1, MKL DNN version: v0.17 (commit hash: 830a10059a018cd2634d94195140cf2d8790a75a, model: https://github.com/intel/caffe/blob/master/models/intel_optimized_models/int8/resnet50_int8_full_conv.prototxt, BS=64, synthetic Data, 4 instance/2 socket, Datatype: INT8 vs Tested by Intel as of July 11th 2017: 2S Intel® Xeon® Platinum 8180 CPU @ 2.50GHz (28 cores), HT disabled, turbo disabled, scaling governor set to “performance” via intel_pstate driver, 384GB DDR4-2666 ECC RAM. CentOS Linux release 7.3.1611 (Core), Linux kernel 3.10.0-514.10.2.el7.x86_64. SSD: Intel® SSD DC S3700 Series (800GB, 2.5in SATA 6Gb/s, 25nm, MLC).Performance measured with: Environment variables: KMP_AFFINITY=’granularity=fine, compact‘, OMP_NUM_THREADS=56, CPU Freq set with cpupower frequency-set -d 2.5G -u 3.8G -g performance. Caffe: (http://github.com/intel/caffe/), revision f96b759f71b2281835f690af267158b82b150b5c. Inference measured with “caffe time –forward_only” command, training measured with “caffe time” command. For “ConvNet” topologies, synthetic dataset was used. For other topologies, data was stored on local storage and cached in memory before training. Topology specs from https://github.com/intel/caffe/tree/master/models/intel_optimized_models(ResNet-50). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“.

Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more information go to www.intel.com/benchmarks.

Performance results are based on testing or projections as of 7/11/2017 to 4/1/2019 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance.

Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel.

Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice (Notice Revision #20110804).

The benchmark results may need to be revised as additional testing is conducted. The results depend on the specific platform configurations and workloads utilized in the testing, and may not be applicable to any particular user’s components, computer system or workloads. The results are not necessarily representative of other benchmarks and other benchmark results may show greater or lesser impact from mitigations.

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.

Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at intel.com.

Intel, the Intel logo, Xeon, Optane, and OpenVINO are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation.

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