EX-99.3

EX-99.3 4 d741646dex993.htm EX-99.3 EX-99.3

Exhibit 99.3 EXECUTIVE VICE PRESIDENT & GENERAL MANAGER DATA CENTER GROUPExhibit 99.3 EXECUTIVE VICE PRESIDENT & GENERAL MANAGER DATA CENTER GROUP

Statements in this presentation that refer to business outlook, future plans and expectations are forward-looking statements that involve a number of risks and uncertainties. Words such as anticipates, expects, intends, goals, plans, believes, seeks, estimates, continues, may, will, “would,” should, “could,” and variations of such words and similar expressions are intended to identify such forward-looking statements. Statements that refer to or are based on estimates, forecasts, projections, uncertain events or assumptions, including statements relating to total addressable market (TAM) or market opportunity, future products and the expected availability and benefits of such products, and anticipated trends in our businesses or the markets relevant to them, also identify forward-looking statements. Such statements are based on management's expectations as of May 8, 2019, unless an earlier date is indicated, and involve many risks and uncertainties that could cause actual results to differ materially from those expressed or implied in these forward-looking statements. Important factors that could cause actual results to differ materially from the company's expectations are set forth in Intel's earnings release dated April 25, 2019, which is included as an exhibit to Intel’s Form 8-K furnished to the SEC on such date. Additional information regarding these and other factors that could affect Intel's results is included in Intel's SEC filings, including the company's most recent reports on Forms 10-K and 10-Q. Copies of Intel's Form 10-K, 10-Q and 8-K reports may be obtained by visiting our Investor Relations website at www.intc.com or the SEC's website at www.sec.gov. All information in this presentation reflects management’s views as of May 8, 2019, unless an earlier date is indicated. Intel does not undertake, and expressly disclaims any duty, to update any statement made in this presentation, whether as a result of new information, new developments or otherwise, except to the extent that disclosure may be required by law.Statements in this presentation that refer to business outlook, future plans and expectations are forward-looking statements that involve a number of risks and uncertainties. Words such as anticipates, expects, intends, goals, plans, believes, seeks, estimates, continues, may, will, “would,” should, “could,” and variations of such words and similar expressions are intended to identify such forward-looking statements. Statements that refer to or are based on estimates, forecasts, projections, uncertain events or assumptions, including statements relating to total addressable market (TAM) or market opportunity, future products and the expected availability and benefits of such products, and anticipated trends in our businesses or the markets relevant to them, also identify forward-looking statements. Such statements are based on management's expectations as of May 8, 2019, unless an earlier date is indicated, and involve many risks and uncertainties that could cause actual results to differ materially from those expressed or implied in these forward-looking statements. Important factors that could cause actual results to differ materially from the company's expectations are set forth in Intel's earnings release dated April 25, 2019, which is included as an exhibit to Intel’s Form 8-K furnished to the SEC on such date. Additional information regarding these and other factors that could affect Intel's results is included in Intel's SEC filings, including the company's most recent reports on Forms 10-K and 10-Q. Copies of Intel's Form 10-K, 10-Q and 8-K reports may be obtained by visiting our Investor Relations website at www.intc.com or the SEC's website at www.sec.gov. All information in this presentation reflects management’s views as of May 8, 2019, unless an earlier date is indicated. Intel does not undertake, and expressly disclaims any duty, to update any statement made in this presentation, whether as a result of new information, new developments or otherwise, except to the extent that disclosure may be required by law.

The Data-centric opportunity is massive LARGEST OPPORTUNITY IN INTEL’S HISTORY, OVER $200B TAM BY 2023 industry Mega-trends LEVERAGE OUR STRENGTHS ARTIFICIAL INTELLIGENCE, CLOUD, CLOUDIFICATION OF NETWORK | EDGE Intel has an unparalleled array of assets to fuel growth PORTFOLIO OF LEADERSHIP PRODUCTS TO MOVE, STORE AND PROCESS DATAThe Data-centric opportunity is massive LARGEST OPPORTUNITY IN INTEL’S HISTORY, OVER $200B TAM BY 2023 industry Mega-trends LEVERAGE OUR STRENGTHS ARTIFICIAL INTELLIGENCE, CLOUD, CLOUDIFICATION OF NETWORK | EDGE Intel has an unparalleled array of assets to fuel growth PORTFOLIO OF LEADERSHIP PRODUCTS TO MOVE, STORE AND PROCESS DATA

GROWTH OF PROLIFERATION OF CLOUDIFICATION OF THEGROWTH OF PROLIFERATION OF CLOUDIFICATION OF THE

AI Analytics HPC Multi-cloud & Orchestration Network in-memory Database Virtualization COMPUTE DEMAND (MIPS) ~60% CAGR Security 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023AI Analytics HPC Multi-cloud & Orchestration Network in-memory Database Virtualization COMPUTE DEMAND (MIPS) ~60% CAGR Security 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

DATA-CENTRIC TAM FORECAST 7% CAGR IOT + AD FPGA NON-VOLATILE MEMORY DATA CENTER MEMORY SILICON PHOTONICS ETHERNET + FABRIC NETWORK LOGIC SILICON Grow Revenue faster than TAM STORAGE LOGIC SILICON SERVER + SERVER-BASED STORAGE LOGIC SILICON MSS 2018 2019 2020 2021 2022 2023DATA-CENTRIC TAM FORECAST 7% CAGR IOT + AD FPGA NON-VOLATILE MEMORY DATA CENTER MEMORY SILICON PHOTONICS ETHERNET + FABRIC NETWORK LOGIC SILICON Grow Revenue faster than TAM STORAGE LOGIC SILICON SERVER + SERVER-BASED STORAGE LOGIC SILICON MSS 2018 2019 2020 2021 2022 2023

INTEL DATA CENTER GROUP REVENUE 12% CAGR ◊ Cloud SP + Comms SP approaching 70% of DCG revenue CLOUD + COMMS ~65% ◊ 2019 revenue forecast down mid-single CLOUD + digits YOY COMMS ~40% ◊ Inventory and capacity absorption off of a record 21% growth year E&G E&G ◊ Continued China weakness ~60% ~35% 2014 2015 2016 2017 2018INTEL DATA CENTER GROUP REVENUE 12% CAGR ◊ Cloud SP + Comms SP approaching 70% of DCG revenue CLOUD + COMMS ~65% ◊ 2019 revenue forecast down mid-single CLOUD + digits YOY COMMS ~40% ◊ Inventory and capacity absorption off of a record 21% growth year E&G E&G ◊ Continued China weakness ~60% ~35% 2014 2015 2016 2017 2018

INTEL PUBLIC CLOUD SP REVENUE >30% CAGR $B Investing to enable Next wave CSPs NEXT WAVE GROWTH OF 33% IN 2018 CAGR NEXT Deepen Partnerships with CSPs WAVE CUSTOM CPUS >55% OF VOLUME IN 2018 Public Cloud Business is TAM Expansive CAGR 2/3 OF REV IS TAM EXPANSIVE, AND GROWING (CONSUMER AND NEW CLOUD SERVICES) SUPER 7 2014 2015 2016 2017 2018INTEL PUBLIC CLOUD SP REVENUE >30% CAGR $B Investing to enable Next wave CSPs NEXT WAVE GROWTH OF 33% IN 2018 CAGR NEXT Deepen Partnerships with CSPs WAVE CUSTOM CPUS >55% OF VOLUME IN 2018 Public Cloud Business is TAM Expansive CAGR 2/3 OF REV IS TAM EXPANSIVE, AND GROWING (CONSUMER AND NEW CLOUD SERVICES) SUPER 7 2014 2015 2016 2017 2018

DIGITAL TRANSFORMATION CONTINUES CLOUD SPS INVESTING IN HYBRID CLOUD SOLUTIONS AWS DELL TECHNOLOGIES OUTPOSTS CLOUD GOOGLE CLOUD MICROSOFT AZURE ANTHOS VMWARE SOLUTIONSDIGITAL TRANSFORMATION CONTINUES CLOUD SPS INVESTING IN HYBRID CLOUD SOLUTIONS AWS DELL TECHNOLOGIES OUTPOSTS CLOUD GOOGLE CLOUD MICROSOFT AZURE ANTHOS VMWARE SOLUTIONS

Move Process Store ETHERNET SILICON PHOTONICS OMNI-PATH FABRIC Software & System-LevelMove Process Store ETHERNET SILICON PHOTONICS OMNI-PATH FABRIC Software & System-Level

ND A P R I L 2 L A U N C H MOVE STORE PROCESS “Only one company can introduce technologies across such a broad set of areas – this is unparalleled.” Mario Morales, IDCND A P R I L 2 L A U N C H MOVE STORE PROCESS “Only one company can introduce technologies across such a broad set of areas – this is unparalleled.” Mario Morales, IDC

CLOUD VIDEO ANALYSIS INDUSTRIAL BIG DATA CLOUD MANAGEMENT 8260 DLBOOST VS FP32 MORE VMS AI 8280+OPTANE PM VS DRAM 8260+OPTANE PM VS DRAM ANALYTICS CLOUD ORCHESTRATION VNETWORK GATEWAY IMDB MORE UP VM TO INSTANCES 5218N+QAT VS 5118 8280+OPTANE PM VS DRAM CUSTOM SKUS STANDARD SKUS NETWORK VIRTUALIZATION IMDB VIRTUAL NG FIREWALL CORES PER SOCKET SOCKETS MEMORY PER SOCKET PHYSICS SIMULATION 8260+OPTANE PM VS DRAM 6230N+QAT VS 6230N up IN-MEMORY DATABASE SECURITY to 9242 VS 8160 AVG. MAINSTREAM PERF GEN ON GEN HPCCLOUD VIDEO ANALYSIS INDUSTRIAL BIG DATA CLOUD MANAGEMENT 8260 DLBOOST VS FP32 MORE VMS AI 8280+OPTANE PM VS DRAM 8260+OPTANE PM VS DRAM ANALYTICS CLOUD ORCHESTRATION VNETWORK GATEWAY IMDB MORE UP VM TO INSTANCES 5218N+QAT VS 5118 8280+OPTANE PM VS DRAM CUSTOM SKUS STANDARD SKUS NETWORK VIRTUALIZATION IMDB VIRTUAL NG FIREWALL CORES PER SOCKET SOCKETS MEMORY PER SOCKET PHYSICS SIMULATION 8260+OPTANE PM VS DRAM 6230N+QAT VS 6230N up IN-MEMORY DATABASE SECURITY to 9242 VS 8160 AVG. MAINSTREAM PERF GEN ON GEN HPC

A PLATFORM APPROACH INTEL® OPTANE™ DC PERSISTENT MEMORY SAM (2023) >50% CAGR (’18-’23) VMS, CONTAINERS, REAL-TIME STORAGE IN-MEMORY CONTENT HIGH PERFORMANCE DATABASE APP DENSITY DELIVERY ANALYTICS DATA REPLICATION COMPUTING CUSTOMER PROOF-OF-CONCEPT TRACTION SINCE LAUNCH FORTUNE 500 SUPER 7 NEXT WAVE CSPs COMMS SPsA PLATFORM APPROACH INTEL® OPTANE™ DC PERSISTENT MEMORY SAM (2023) >50% CAGR (’18-’23) VMS, CONTAINERS, REAL-TIME STORAGE IN-MEMORY CONTENT HIGH PERFORMANCE DATABASE APP DENSITY DELIVERY ANALYTICS DATA REPLICATION COMPUTING CUSTOMER PROOF-OF-CONCEPT TRACTION SINCE LAUNCH FORTUNE 500 SUPER 7 NEXT WAVE CSPs COMMS SPs

nd Cooper Lake Intel® Xeon® Intel® Xeon® Intel® Xeon® 2 Gen Intel® Xeon® & Processor E5 v3 Processor E5 v4 Scalable Processor Scalable Processor Haswell Broadwell Skylake Cascade Lake Ice Lake PRODUCTION SHIPMENTS 1H’20 SAMPLES SHIPPING NOW POWERED ON AT MULTIPLE CUSTOMERSnd Cooper Lake Intel® Xeon® Intel® Xeon® Intel® Xeon® 2 Gen Intel® Xeon® & Processor E5 v3 Processor E5 v4 Scalable Processor Scalable Processor Haswell Broadwell Skylake Cascade Lake Ice Lake PRODUCTION SHIPMENTS 1H’20 SAMPLES SHIPPING NOW POWERED ON AT MULTIPLE CUSTOMERS

nd Cooper Lake Sapphire Next Intel® Xeon® Intel® Xeon® Intel® Xeon® 2 Gen Intel® Xeon® & Processor E5 v3 Processor E5 v4 Scalable Processor Scalable Processor Rapids Gen Haswell Broadwell Skylake Cascade Lake Ice Lake DRIVING LEADERSHIP WORKLOAD PERFORMANCE Moving to QUARTER CADENCE QUARTER CADENCEnd Cooper Lake Sapphire Next Intel® Xeon® Intel® Xeon® Intel® Xeon® 2 Gen Intel® Xeon® & Processor E5 v3 Processor E5 v4 Scalable Processor Scalable Processor Rapids Gen Haswell Broadwell Skylake Cascade Lake Ice Lake DRIVING LEADERSHIP WORKLOAD PERFORMANCE Moving to QUARTER CADENCE QUARTER CADENCE

AI DATA CENTER SI TAM >20% CAGR ’18 INTEL DATA CENTER AI REV INFERENCE TRAINING 2018 2023AI DATA CENTER SI TAM >20% CAGR ’18 INTEL DATA CENTER AI REV INFERENCE TRAINING 2018 2023

FROM CPU TO XPU - ONE SIZE DOES NOT FIT ALL Intel® Xeon® Intel® Intel® Intel® Nervana™ NNP Scalable Processor Family Discrete Graphics FPGA Intel® Movidius™ Myriad™ Intel® Mobileye® EyeQ® ONEAPI Unified developer frameworkFROM CPU TO XPU - ONE SIZE DOES NOT FIT ALL Intel® Xeon® Intel® Intel® Intel® Nervana™ NNP Scalable Processor Family Discrete Graphics FPGA Intel® Movidius™ Myriad™ Intel® Mobileye® EyeQ® ONEAPI Unified developer framework

ONLY CPU WITH BUILT-IN INFERENCE ACCELERATION INTEL OPTIMIZATION FOR CAFFE RESNET-50 intel® xeon® platinum intel® xeon® platinum INTEL DL BOOST 5.7 SUPPORTED IN ALL MAJOR FRAMEWORKS INTEL AVX-512 1.0 JUL'17 DEC'18 APR'19 COLUMN1 BASE VS BASE VS BASE intel® xeon® platinum 8100 processor INFERENCE THROUGHPUT (IMAGES/SEC)ONLY CPU WITH BUILT-IN INFERENCE ACCELERATION INTEL OPTIMIZATION FOR CAFFE RESNET-50 intel® xeon® platinum intel® xeon® platinum INTEL DL BOOST 5.7 SUPPORTED IN ALL MAJOR FRAMEWORKS INTEL AVX-512 1.0 JUL'17 DEC'18 APR'19 COLUMN1 BASE VS BASE VS BASE intel® xeon® platinum 8100 processor INFERENCE THROUGHPUT (IMAGES/SEC)

Silicon Including Partnering with Industry leading Deep LearningSilicon Including Partnering with Industry leading Deep Learning

Autonomous cars things Access | network Edge Core network Data Center | Cloud EDGE COMPUTING NETWORK EDGE Autonomous Driving IOT Network SILICON + SERVICES SILICON SILICON 2018 intel Revenue ~$65B opportunity By 2023 >20% GROWTH YOYAutonomous cars things Access | network Edge Core network Data Center | Cloud EDGE COMPUTING NETWORK EDGE Autonomous Driving IOT Network SILICON + SERVICES SILICON SILICON 2018 intel Revenue ~$65B opportunity By 2023 >20% GROWTH YOY

SENIOR VICE PRESIDENT & GENERAL MANAGER NETWORK PLATFORMS GROUPSENIOR VICE PRESIDENT & GENERAL MANAGER NETWORK PLATFORMS GROUP

Traditional network intelligent network THINGS NETWORK Data Center/ things RAN/Edge CORE Cloud Analytics compute Compute Compute Compute Compute Network Storage Storage Storage Storage Analytics Analytics Analytics Analytics Storage Scalability & flexibility for networking workloads ETHERNET OPENNESS SILICON PHOTONICSTraditional network intelligent network THINGS NETWORK Data Center/ things RAN/Edge CORE Cloud Analytics compute Compute Compute Compute Compute Network Storage Storage Storage Storage Analytics Analytics Analytics Analytics Storage Scalability & flexibility for networking workloads ETHERNET OPENNESS SILICON PHOTONICS

2011 2013 2015 2017 2018 2019 st st NFV 1 NFV 20% DPDK 65% 1 100% DEFINED PROOF OF OF COMMS SPS MOVES TO LINUX CORE NW FUNCTIONS CLOUD-NATIVE CONCEPTS ADOPT NFV FOUNDATION VIRTUALIZED NETWORK st World’s 1 End to end cloud native Mobile network IMPROVED FOR ENTIRE OPERATIONS FROM CONCEPT TO NETWORK TCO NETWORK STAFF DEPLOYMENT ARCHITECTURE ON INTEL ARCHITECTURE “Our vision is to build a network that innovates at the speed of software and scales at the speed of cloud… leveraging best-in-class technology…to provide a high quality, cost- effective service to our customers.” Tareq Amin, Group CTIO, Rakuten 2011 2013 2015 2017 2018 2019 st st NFV 1 NFV 20% DPDK 65% 1 100% DEFINED PROOF OF OF COMMS SPS MOVES TO LINUX CORE NW FUNCTIONS CLOUD-NATIVE CONCEPTS ADOPT NFV FOUNDATION VIRTUALIZED NETWORK st World’s 1 End to end cloud native Mobile network IMPROVED FOR ENTIRE OPERATIONS FROM CONCEPT TO NETWORK TCO NETWORK STAFF DEPLOYMENT ARCHITECTURE ON INTEL ARCHITECTURE “Our vision is to build a network that innovates at the speed of software and scales at the speed of cloud… leveraging best-in-class technology…to provide a high quality, cost- effective service to our customers.” Tareq Amin, Group CTIO, Rakuten

ACCELERATED BY 5G INTEL NETWORK REVENUE ~40% CAGR 22% MSS DRIVING TRANSFORMATION TO CPU VOLUME ~20% CAGR CLOUD-BASED PLATFORMS XEON ASP 10% CAGR DELIVERING PORTFOLIO OF PRODUCTS FOR 5G AND EDGE ON TRACK TO BASESTATION MSS 8% MSS >40% BY 2022 2014 2016 2018ACCELERATED BY 5G INTEL NETWORK REVENUE ~40% CAGR 22% MSS DRIVING TRANSFORMATION TO CPU VOLUME ~20% CAGR CLOUD-BASED PLATFORMS XEON ASP 10% CAGR DELIVERING PORTFOLIO OF PRODUCTS FOR 5G AND EDGE ON TRACK TO BASESTATION MSS 8% MSS >40% BY 2022 2014 2016 2018

INTEL IOTG REVENUE >10% CAGR 16% MSS AGGREGATION AT THE EDGE ASP VIDEO INFERENCE HIGH PERFORMANCE COMPUTE ATOM CORE XEON 2014 2015 2016 2017 2018INTEL IOTG REVENUE >10% CAGR 16% MSS AGGREGATION AT THE EDGE ASP VIDEO INFERENCE HIGH PERFORMANCE COMPUTE ATOM CORE XEON 2014 2015 2016 2017 2018

INTEL IOTG + AD REVENUE >15% CAGR Extending into MOBILEYE 2014 2015 2016 2017 2018INTEL IOTG + AD REVENUE >15% CAGR Extending into MOBILEYE 2014 2015 2016 2017 2018

Configuration Disclosure Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. See configuration disclosure for details. No product or component can be absolutely secure. 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 complete information visit www.intel.com/benchmarks. Up to 1.33x average generational gains on mainstream Gold SKU: Geomean of est SPECrate2017_int_base, est SPECrate2017_fp_base, Stream Triad, Intel Distribution of Linpack, server side Java. Gold 5218 vs Gold 5118: 1-node, 2x Intel® Xeon® Gold 5218 cpu on Wolf Pass with 384 GB (12 X 32GB 2933 (2666)) total memory, ucode 0x4000013 on RHEL7.6, 3.10.0-957.el7.x86_64, IC18u2, AVX2, HT on all (off Stream, Linpack), Turbo on, result: est int throughput=162, est fp throughput=172, Stream Triad=185, Linpack=1088, server side java=98333, test by Intel on 12/7/2018. 1-node, 2x Intel® Xeon® Gold 5118 cpu on Wolf Pass with 384 GB (12 X 32GB 2666 (2400)) total memory, ucode 0x200004D on RHEL7.6, 3.10.0-957.el7.x86_64, IC18u2, AVX2, HT on all (off Stream, Linpack), Turbo on, result: est int throughput=119, est fp throughput=134, Stream Triad=148.6, Linpack=822, server side java=67434, test by Intel on 11/12/2018. 2.01x LS-Dyna* Explicit, 3car: 1-node, 2x Intel® Xeon® Platinum 8160L cpu on Wolf Pass with 192 GB (12 slots / 16GB / 2666) total memory, ucode 0x200004d on Oracle Linux Server release 7.6 , 3.10.0-862.14.4.el7.crt1.x86_64, Intel SSDSC2BA80, LS-Dyna 9.3-Explicit AVX2 binary, 3car, HT on, Turbo on, test by Intel on 2/26/2019. 1-node, 2x Intel® Xeon® Platinum 9242 cpu on Intel reference platform with 384 GB (24 slots / 16GB / 2933) total memory, ucode 0x4000017 on CentOS 7.6, 3.10.0- 957.5.1.el7.x86_64, Intel SSDSC2BA80, LS-Dyna 9.3-Explicit AVX2 binary, 3car, HT on, Turbo on, test by Intel on 3/18/2019. 1.39x BAOSIGHT* xInsight*: 1-node, 2x Intel® Xeon® Platinum 8260L cpu on S2600WFS with 768 DDR GB (24 slots / 32GB / 2666) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 480GB SSD OS Drive, 1x Intel XC722, xInsight 2.0 internal workload, HT on, Turbo on, test by Intel/Baosight on 1/8/2019. 1-node, 2x Intel® Xeon® Platinum 8260L cpu on S2600WFS with 192 DDR + 1024 Intel DCPMM GB (12 slots / 16 GB / 2666 DDR + 8 slots / 128 GB / 2666 Intel DCPMM) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 480GB SSD OS Drive, 1x Intel XC722, xInsight 2.0 internal workload, HT on, Turbo on, test by Intel/Baosight on 1/9/2018. 1.42x Huawei* FusionSphere*: 1-node, 2x Intel® Xeon® Platinum 8260L cpu on Wolf Pass with 1024 GB (16 slots / 64GB / 2666) total memory, ucode 0x400000A on FusionSphere HyperV, 3.10.0-514.44.5.10_96.x86_64 , 1x Intel 800GB SSD OS Drive, 1x Intel 800GB SSD OS Drive, 1x Intel XC722, FusionSphere 6.3.1, mysql-5.7.24, sysbench-1.0.6, HT on, Turbo on, test by Huawei/Intel on 1/11/2018. 1-node, 2x Intel® Xeon® Platinum 8260L cpu on Wolf Pass with 384 DDR + 1536 Intel DCPMM GB (12 slots / 32 GB / 2666 DDR + 12 slots / 128 GB / 2666 Intel DCPMM) total memory, ucode 0x400000A on FusionSphere HyperV, 3.10.0-514.44.5.10_96.x86_64 , 3 x P3520 1.8TB Application Data, 3 x P3520 1.8TB Application Data, 1x Intel XC722, FusionSphere 6.3.1, mysql- 5.7.24, sysbench-1.0.6, HT on, Turbo on, test by Huawei/Intel on 1/11/2018. 1.35x GBASE: 1-node, 2x Intel® Xeon® Platinum 8260 cpu on S2600WFT with 768 DDR GB (24 slots / 32GB / 2666) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 400GB SSD OS Drive, 1x Intel XC722, Gbase 8m 6.3.2 OCS Benchmark, HT on, Turbo on, test by GBASE/Intel on 2/19/2019. 1-node, 2x Intel® Xeon® Platinum 8260 cpu on S2600WFT with 192 DDR + 1024 Intel DCPMM GB (12 slots / 16 GB / 2666 DDR + 8 slots / 128 GB / 2666 Intel DCPMM) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 400GB SSD OS Drive, 1x Intel XC722, Gbase 8m 6.3.2 OCS Benchmark, HT on, Turbo on, test by GBASE/Intel on 2/19/2019. 2x Nokia* SDWAN: Configuration #1 (With Intel® QuickAssist® Technology): 2x Intel® Xeon® Gold 5218N Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2667MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; 1x Intel® QuickAssist Adapter 8970, Cipher: AES-128 SHA-256; Intel® Ethernet Converged Network Adapter X520-SR2; Application: Nokia Nuage SDWAN NSGv 5.3.3U3. Configuration # 2: 2x Intel® Xeon® Gold 5118 Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2667MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; Intel® Ethernet Converged Network Adapter X520-SR2; Application: Nokia Nuage SDWAN NSGv 5.3.3U3. Results recorded by Intel on 2/14/2018 in collaborate with Nokia. 3.26x latency reduction for Tencent* Cloud Video Analysis: Tested by Tencent as of 1/14/2019. 2 socket Intel® Xeon® Gold Processor, 24 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2666 MHz), CentOS 7.6 3.10.0-957.el7.x86_64, Compiler: gcc 4.8.5, Deep Learning Framework: Intel® Optimizations for Caffe v1.1.3, Topology: modified inception v3, Tencent’s private dataset, BS=1. Comparing performance on same system with FP32 vs INT8 w/ Intel® DL Boost 3x Fortinet* Fortigate*: Configuration #1 (With Intel® QuickAssist Technology) 2x Intel® Xeon® Gold E5-6230N Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2933MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; 1 x Intel® QuickAssist Adapter 8970, IPSec AES128-SHA256; 1 x Dual Port 40GbE Intel® Ethernet Network Adapter XL710; Application: FortiGate VM64- KVM (v.6.2.0 interim build). Configuration #2 (Without Intel® QuickAssist Technology) : 2x Intel® Xeon® Gold E5-6230N Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2933MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; 1 x Dual Port 40GbE Intel® Ethernet Network Adapter XL710; Application: FortiGate VM64-KVM (v.6.2.0 interim build). Results recorded by Intel and reviewed by Fortinet on 3/27/2018. Up to 8X more VMs when running Redis with 8X memory capacity: 1-node, 2x Intel Xeon Platinum 8276 cpu on Intel reference platform with 768 GB (12 slots / 32GB / 2666) total memory, BIOS PLYXCRB1.86B.0573.D10.1901300453 on Fedora-27, 4.20.4- 200.fc29.x86_64, 2x40G, Redis 4.0.11, memtier_benchmark-1.2.12 (80/20 read/write); 1K record size, KVM, 1/VM, centos-7.0, HT on, Turbo on, test by Intel on 2/22/2019. 1-node, 2x Intel Xeon Platinum 8276 cpu on Intel reference platform with 192 + 6144 GB (12 slots / 16GB / 2666 DDR + 12 slots / 512GB/ 2666 Intel Optance DCPMM) total memory, BIOS PLYXCRB1.86B.0573.D10.1901300453 on Fedora-27, 4.20.4-200.fc29.x86_64, 2x40G, Redis 4.0.11, memtier_benchmark-1.2.12 (80/20 read/write); 1K record size, KVM, 1/VM, centos-7.0, Memory mode, HT on, Turbo on, test by Intel on 2/22/2019.Configuration Disclosure Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. See configuration disclosure for details. No product or component can be absolutely secure. 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 complete information visit www.intel.com/benchmarks. Up to 1.33x average generational gains on mainstream Gold SKU: Geomean of est SPECrate2017_int_base, est SPECrate2017_fp_base, Stream Triad, Intel Distribution of Linpack, server side Java. Gold 5218 vs Gold 5118: 1-node, 2x Intel® Xeon® Gold 5218 cpu on Wolf Pass with 384 GB (12 X 32GB 2933 (2666)) total memory, ucode 0x4000013 on RHEL7.6, 3.10.0-957.el7.x86_64, IC18u2, AVX2, HT on all (off Stream, Linpack), Turbo on, result: est int throughput=162, est fp throughput=172, Stream Triad=185, Linpack=1088, server side java=98333, test by Intel on 12/7/2018. 1-node, 2x Intel® Xeon® Gold 5118 cpu on Wolf Pass with 384 GB (12 X 32GB 2666 (2400)) total memory, ucode 0x200004D on RHEL7.6, 3.10.0-957.el7.x86_64, IC18u2, AVX2, HT on all (off Stream, Linpack), Turbo on, result: est int throughput=119, est fp throughput=134, Stream Triad=148.6, Linpack=822, server side java=67434, test by Intel on 11/12/2018. 2.01x LS-Dyna* Explicit, 3car: 1-node, 2x Intel® Xeon® Platinum 8160L cpu on Wolf Pass with 192 GB (12 slots / 16GB / 2666) total memory, ucode 0x200004d on Oracle Linux Server release 7.6 , 3.10.0-862.14.4.el7.crt1.x86_64, Intel SSDSC2BA80, LS-Dyna 9.3-Explicit AVX2 binary, 3car, HT on, Turbo on, test by Intel on 2/26/2019. 1-node, 2x Intel® Xeon® Platinum 9242 cpu on Intel reference platform with 384 GB (24 slots / 16GB / 2933) total memory, ucode 0x4000017 on CentOS 7.6, 3.10.0- 957.5.1.el7.x86_64, Intel SSDSC2BA80, LS-Dyna 9.3-Explicit AVX2 binary, 3car, HT on, Turbo on, test by Intel on 3/18/2019. 1.39x BAOSIGHT* xInsight*: 1-node, 2x Intel® Xeon® Platinum 8260L cpu on S2600WFS with 768 DDR GB (24 slots / 32GB / 2666) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 480GB SSD OS Drive, 1x Intel XC722, xInsight 2.0 internal workload, HT on, Turbo on, test by Intel/Baosight on 1/8/2019. 1-node, 2x Intel® Xeon® Platinum 8260L cpu on S2600WFS with 192 DDR + 1024 Intel DCPMM GB (12 slots / 16 GB / 2666 DDR + 8 slots / 128 GB / 2666 Intel DCPMM) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 480GB SSD OS Drive, 1x Intel XC722, xInsight 2.0 internal workload, HT on, Turbo on, test by Intel/Baosight on 1/9/2018. 1.42x Huawei* FusionSphere*: 1-node, 2x Intel® Xeon® Platinum 8260L cpu on Wolf Pass with 1024 GB (16 slots / 64GB / 2666) total memory, ucode 0x400000A on FusionSphere HyperV, 3.10.0-514.44.5.10_96.x86_64 , 1x Intel 800GB SSD OS Drive, 1x Intel 800GB SSD OS Drive, 1x Intel XC722, FusionSphere 6.3.1, mysql-5.7.24, sysbench-1.0.6, HT on, Turbo on, test by Huawei/Intel on 1/11/2018. 1-node, 2x Intel® Xeon® Platinum 8260L cpu on Wolf Pass with 384 DDR + 1536 Intel DCPMM GB (12 slots / 32 GB / 2666 DDR + 12 slots / 128 GB / 2666 Intel DCPMM) total memory, ucode 0x400000A on FusionSphere HyperV, 3.10.0-514.44.5.10_96.x86_64 , 3 x P3520 1.8TB Application Data, 3 x P3520 1.8TB Application Data, 1x Intel XC722, FusionSphere 6.3.1, mysql- 5.7.24, sysbench-1.0.6, HT on, Turbo on, test by Huawei/Intel on 1/11/2018. 1.35x GBASE: 1-node, 2x Intel® Xeon® Platinum 8260 cpu on S2600WFT with 768 DDR GB (24 slots / 32GB / 2666) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 400GB SSD OS Drive, 1x Intel XC722, Gbase 8m 6.3.2 OCS Benchmark, HT on, Turbo on, test by GBASE/Intel on 2/19/2019. 1-node, 2x Intel® Xeon® Platinum 8260 cpu on S2600WFT with 192 DDR + 1024 Intel DCPMM GB (12 slots / 16 GB / 2666 DDR + 8 slots / 128 GB / 2666 Intel DCPMM) total memory, ucode 0x400000A on CentOS 7.5, 3.10.0-957.1.3.el7.x86_64, 1x Intel 400GB SSD OS Drive, 1x Intel XC722, Gbase 8m 6.3.2 OCS Benchmark, HT on, Turbo on, test by GBASE/Intel on 2/19/2019. 2x Nokia* SDWAN: Configuration #1 (With Intel® QuickAssist® Technology): 2x Intel® Xeon® Gold 5218N Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2667MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; 1x Intel® QuickAssist Adapter 8970, Cipher: AES-128 SHA-256; Intel® Ethernet Converged Network Adapter X520-SR2; Application: Nokia Nuage SDWAN NSGv 5.3.3U3. Configuration # 2: 2x Intel® Xeon® Gold 5118 Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2667MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; Intel® Ethernet Converged Network Adapter X520-SR2; Application: Nokia Nuage SDWAN NSGv 5.3.3U3. Results recorded by Intel on 2/14/2018 in collaborate with Nokia. 3.26x latency reduction for Tencent* Cloud Video Analysis: Tested by Tencent as of 1/14/2019. 2 socket Intel® Xeon® Gold Processor, 24 cores HT On Turbo ON Total Memory 192 GB (12 slots/ 16GB/ 2666 MHz), CentOS 7.6 3.10.0-957.el7.x86_64, Compiler: gcc 4.8.5, Deep Learning Framework: Intel® Optimizations for Caffe v1.1.3, Topology: modified inception v3, Tencent’s private dataset, BS=1. Comparing performance on same system with FP32 vs INT8 w/ Intel® DL Boost 3x Fortinet* Fortigate*: Configuration #1 (With Intel® QuickAssist Technology) 2x Intel® Xeon® Gold E5-6230N Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2933MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; 1 x Intel® QuickAssist Adapter 8970, IPSec AES128-SHA256; 1 x Dual Port 40GbE Intel® Ethernet Network Adapter XL710; Application: FortiGate VM64- KVM (v.6.2.0 interim build). Configuration #2 (Without Intel® QuickAssist Technology) : 2x Intel® Xeon® Gold E5-6230N Processor on Neon City Platform with 192 GB total memory (12 slots / 16GB / DDR4 2933MHz), ucode 0x4000019, Bios: PLYXCRB 1.86B.0568.D10.1901032132, uCode: 0x4000019 on CentOS 7.5 with Kernel 3.10.0-862, KVM Hypervisor; 1 x Dual Port 40GbE Intel® Ethernet Network Adapter XL710; Application: FortiGate VM64-KVM (v.6.2.0 interim build). Results recorded by Intel and reviewed by Fortinet on 3/27/2018. Up to 8X more VMs when running Redis with 8X memory capacity: 1-node, 2x Intel Xeon Platinum 8276 cpu on Intel reference platform with 768 GB (12 slots / 32GB / 2666) total memory, BIOS PLYXCRB1.86B.0573.D10.1901300453 on Fedora-27, 4.20.4- 200.fc29.x86_64, 2x40G, Redis 4.0.11, memtier_benchmark-1.2.12 (80/20 read/write); 1K record size, KVM, 1/VM, centos-7.0, HT on, Turbo on, test by Intel on 2/22/2019. 1-node, 2x Intel Xeon Platinum 8276 cpu on Intel reference platform with 192 + 6144 GB (12 slots / 16GB / 2666 DDR + 12 slots / 512GB/ 2666 Intel Optance DCPMM) total memory, BIOS PLYXCRB1.86B.0573.D10.1901300453 on Fedora-27, 4.20.4-200.fc29.x86_64, 2x40G, Redis 4.0.11, memtier_benchmark-1.2.12 (80/20 read/write); 1K record size, KVM, 1/VM, centos-7.0, Memory mode, HT on, Turbo on, test by Intel on 2/22/2019.

Configuration Disclosure Intel® Deep Learning Boost th 1x inference throughput baseline on Intel® Xeon® Platinum 8180 processor (July 2017) : Tested by Intel as of July 11 2017: Platform: 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),and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“. th 5.7x inference throughput improvement on Intel® Xeon® Platinum 8180 processor (December 2018) with continued optimizations : Tested by Intel as of November 11 2018 :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.46GB (12slots / 32 GB / 2666 MHz). CentOS Linux-7.3.1611-Core, kernel: 3.10.0-862.3.3.el7.x86_64, SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework Intel® Optimization for caffe version: 551a53d63a6183c233abaa1a19458a25b672ad41 Topology::ResNet_50_v1 BIOS:SE5C620.86B.00.01.0014.070920180847 MKLDNN: 4e333787e0d66a1dca1218e99a891d493dbc8ef1 instances: 2 instances socket:2 (Results on Intel® Xeon® Scalable Processor were measured running multiple instances of the framework. Methodology described here: th https://software.intel.com/en-us/articles/boosting-deep-learning-training-inference-performance-on-xeon-and-xeon-phi) Synthetic data. Datatype: INT8 Batchsize=64 vs Tested by Intel as of July 11 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“. 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, th Datatype: INT8 vs Tested by Intel as of July 11 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“. 2x More inference throughput improvement on Intel® Xeon® Platinum 9282 processor with Intel® DL Boost : Tested by Intel as of 2/26/2019. Platform: Dragon rock 2 socket Intel® Xeon® Platinum 9282(56 cores per socket), HT ON, turbo ON, Total Memory 768 GB (24 slots/ 32 GB/ 2933 MHz), BIOS:SE5C620.86B.0D.01.0241.112020180249, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Deep Learning Framework: Intel® Optimization for Caffe version: https://github.com/intel/caffe d554cbf1, ICC 2019.2.187, 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, No datalayer syntheticData:3x224x224, 56 instance/2 socket, Datatype: INT8 vs Tested by Intel as of th July 11 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“. Configuration Disclosure Intel® Deep Learning Boost th 1x inference throughput baseline on Intel® Xeon® Platinum 8180 processor (July 2017) : Tested by Intel as of July 11 2017: Platform: 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),and https://github.com/soumith/convnet-benchmarks/tree/master/caffe/imagenet_winners (ConvNet benchmarks; files were updated to use newer Caffe prototxt format but are functionally equivalent). Intel C++ compiler ver. 17.0.2 20170213, Intel MKL small libraries version 2018.0.20170425. Caffe run with “numactl -l“. th 5.7x inference throughput improvement on Intel® Xeon® Platinum 8180 processor (December 2018) with continued optimizations : Tested by Intel as of November 11 2018 :2 socket Intel(R) Xeon(R) Platinum 8180 CPU @ 2.50GHz / 28 cores HT ON , Turbo ON Total Memory 376.46GB (12slots / 32 GB / 2666 MHz). CentOS Linux-7.3.1611-Core, kernel: 3.10.0-862.3.3.el7.x86_64, SSD sda RS3WC080 HDD 744.1GB,sdb RS3WC080 HDD 1.5TB,sdc RS3WC080 HDD 5.5TB , Deep Learning Framework Intel® Optimization for caffe version: 551a53d63a6183c233abaa1a19458a25b672ad41 Topology::ResNet_50_v1 BIOS:SE5C620.86B.00.01.0014.070920180847 MKLDNN: 4e333787e0d66a1dca1218e99a891d493dbc8ef1 instances: 2 instances socket:2 (Results on Intel® Xeon® Scalable Processor were measured running multiple instances of the framework. Methodology described here: th https://software.intel.com/en-us/articles/boosting-deep-learning-training-inference-performance-on-xeon-and-xeon-phi) Synthetic data. Datatype: INT8 Batchsize=64 vs Tested by Intel as of July 11 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“. 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, th Datatype: INT8 vs Tested by Intel as of July 11 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“. 2x More inference throughput improvement on Intel® Xeon® Platinum 9282 processor with Intel® DL Boost : Tested by Intel as of 2/26/2019. Platform: Dragon rock 2 socket Intel® Xeon® Platinum 9282(56 cores per socket), HT ON, turbo ON, Total Memory 768 GB (24 slots/ 32 GB/ 2933 MHz), BIOS:SE5C620.86B.0D.01.0241.112020180249, Centos 7 Kernel 3.10.0-957.5.1.el7.x86_64, Deep Learning Framework: Intel® Optimization for Caffe version: https://github.com/intel/caffe d554cbf1, ICC 2019.2.187, 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, No datalayer syntheticData:3x224x224, 56 instance/2 socket, Datatype: INT8 vs Tested by Intel as of th July 11 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“.

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