- Dec 25, 2013
- 3,899
- 193
- 106
Intel Data-Centric Innovation Summit
Videos and PDFs: http://intelstudios.edgesuite.net/180808_dcis/index.html
Intel Newsroom: https://newsroom.intel.com/editorials/data-centric-innovation-summit/
Navin Shenoy: Opening Keynote
*Will give update on roadmap and “how we will win”.
*~50% of revenue is from data-centric and growing double digits
*90% of world’s data created in last 2 years
*example: automotive -> example of end-to-end compute problem (AI, cloud, edge, networking)
*Intel TAM: forecast from IM2017: 2021 $160B data-centric TAM shared // forecast NOW: $200B data-centric https://images.anandtech.com/doci/13192/1533744704225791588519.jpg
*2/3 of cloud is TAM expansion (growing at 40% in H1’18)
*Cloud. 50% of CSP (cloud service provider) CPU volume is a custom CPU, up from 18% in 2013 (“highly customized products for their needs”)
*Networking: cloudification of network (core, access and edge). 20% MSS today of $24B 2022 silicon TAM. ISA optimizations, software, ecosystem, low-power hardware, etc. Example: 1 million base stations sold every year -> imagine Xeons in every one of them.
*AI. $2.5B market in 2017, growing 30% CAGR through 2022 to up to $10B -> investing heavily, biggest incremental investment of Intel
*DC network traffic (e.g. data moving from rack to rack): growing 25% CAGR -> connectivity becoming bottleneck -> full portfolio ($4B TAM growing to $11B in 2022): omni-path fabric, ethernet (#1 MSS, adding “SmartNIC” Cascade Glacier in 2019 combining FPGA and ethernet portfolio), and silicon photonics (Intel’s unique advantage: integrate laser in silicon)
*Moving data: Optane SSD and DIMMs (persistent memory) -> changing memory hierarchy. First QLC consumer and data center solution. Optane SSDs with 40x lower latency and higher endurance. Optane persistent memory.
*Optane persistent memory: $10B 2022 SAM (subset of 2022 DC DRAM market): big performance increase in certain workloads (8-11x). Persistency: from minutes to seconds of reboot times, from three 9s to five 9s of availability. A lot of work required, like software and “Revamp the IMC on the CPU”.
*Google: SAP HANA workload with persistent memory and Cascade Lake
*Announces first production modules of Optane PM shipped yesterday out of the factory -> Optane PM entering production!
*Congratulates engineers working on Optane PM, some working for over a decade to make it reality
*Process everything: Xeon, FPGA and Nervana ASIC.
*20th anniversary of Xeon CPU family -> industry leading processor for data center processing everywher: 220M Xeons, $130B revenue. Tailor Xeon for customer workloads and continue advancements. https://images.anandtech.com/doci/13192/15337460999211370399119.jpg
*Xeon Scalable stats: https://images.anandtech.com/doci/13192/1533746164839496564550.jpg.
*Performance leadership: up to 1.48x per core, 1.72x for L3 packet forwarding, 3.2x Linpack, 1.85x database, 1.45x memory caching
*Reinventing Xeon for AI: inference and training improved by >200x with hardware and software innovations from HSW to SKL
*Through software optimizations in last year: inference improved by 5.4x (INT8) since July’17
*2017: >$1B in revenue from Xeon AI revenue and big growth
*Next-gen: Cascade Lake. Ship in late 2018. New integrated memory controller for Optane PM. Spectre and Meltdown protections. New cache, new instructions.
*Adding new AI extensions: Intel Deep Learning Boost -> extends AVX-512 -> doubles performances (this is already in Knights Mill as VNNI instructions) https://images.anandtech.com/doci/13192/15337465541401312204600.jpg
*Google demo of 11x speed-up from SKX to CSX. A 10x improvement in one year through HW/SW is pretty good.
*Intel Select Solutions: verify configuration: engineer, validate and test solutions at HW and SW level -> makes easy to deploy complex solutions for faster TTM -> for analytics, network, HPC,… Announcement: new Select Solutions around AI, Blockchain and SAP HANA (Optane PM)
*The Intel differentiation: harness all of Intel’s assets (from transistor, architecture, memory, interconnect, security to software) -> welcome Jim Keller to stage, SVP of Engineering
*Came to Intel: amazing opportunity, scale, excellence of engineering, met many people, likes diversity of technology. Working at now: focus on 14nm and 10nm products coming to market, getting up to speed on the process, performance, yield.
*Roadmap talk
. End of 2019: Cooper Lake on 14nm with “next-gen DLBoost”: Bfloat16 (already announced by Naveen at the AI day). 2020 (“fast follow-on to CPL”): Ice Lake. So CPL/ICL is one platform. This for instance means CPL has 8ch memory. Note: as was already leaked, Cooper Lake will by a 3-die MCP, I presume this means 3*24 cores for 72 cores. For those taking notes, that is more than Rome’s 64. https://twitter.com/Ashraf__Eassa/status/1027236588911124480
*Cooper Lake has “significantly better generation on generation improvement”.
*Navin: “Maintain our leadership position in the data center.”
Introduces Naveen Rao!
Naveen Rao: Unlocking Data Value with AI
*Want to make AI more broadly adopted
*Market: $2.5B in 2017, $8-10B in 2022: about evenly split between training/inference, but line between inference/training will become more blurred
*2012: 16.000 CPUs for 2 weeks proof of concept, now applying to real problems
*Intel’s heterogenous portfolio for AI from edge to data center, with shared IP portfolio https://images.anandtech.com/doci/13192/15337477631951431458216.jpg
*Reiterates AI is a $1B business for Intel today (only Xeon in data center, number doesn’t include FPGA and IoT)
*AI development lifecycle. 30% of development time is training, done by GPUs today. https://images.anandtech.com/doci/13192/1533748089180597043906.jpg
*AI roadmap. Debunks GPU myth. “Reality is almost all inference in the world runs on Xeon today”. Reiterates 5.4x inference performance since July’17 (and 1.4x for training), and 11x with Cascade Lake -> focus on lower precision (bfloat16 with Cooper Lake)
*NNP for inference: in development.
*NNP for training, coming in late 2019 NNP L-1000: bloat16, 3-4x performance of Lake Crest, high-bandwidth, low-latency interconnects. -> what really matters is performance (time to train) and efficiency https://images.anandtech.com/doci/13192/1533748403816342061948.jpg
*50% of AIPG (AI Products Group) is dedicated to software (libraries like MKL-DNN) -> but wants to make AI accessible to broad enterprise customers -> even higher level of abstraction for edge: OpenVINO toolkit and Movidius SDK https://images.anandtech.com/doci/13192/1533748664844850855111.jpg
*nGraph. Deep learning compiler nGraph: connects all hardware platforms (Xeon, Movidius, FPGA, GPU, etc.) to all software platforms (TensorFlow, etc.) https://images.anandtech.com/doci/13192/15337487256991267737489.jpg
*Example: Novartis (1028x1280x3) drug discovery: much higher resolution than Imagenet (224x224x3) -> >6x increase in performance by going from 1 to 8 CPUs (using omni-path), and 20x improvement with SW improvements
*Xeon has TCO benefit because it can be amortized over other things than just purely AI workload
*AI Builer Program with “vibrant ecosystem” https://images.anandtech.com/doci/13192/1533749058963243119428.jpg
*Open source community: NLP Architect, nGraph, Coach, Distiller
*AI Academy and DevCloud (110K developers, 150k users each month)
*Expanding AI DevCon (introduced in May)
RaeJeanne Skillern (GM of CSP Group): Cloud: Driving Transformation & Growth
*Three learnings: (1) One size does not fit all. (2) More than just better efficiency: differentiation. (3) Long-term partnerships at scale takes time.
*Cloud revenue stats (43% of revenue in H1’18): https://images.anandtech.com/doci/13192/15337495883751562984527.jpg
*Makes sure all of the Xeon compute capabilities are exploited by the CSPs
*In addition to standard roadmap: custom SKUs (>50% are custom). Xeon-D created out of joint innovation with Facebook. Nearly 30 off-roadmap, customized SKUs (for instance custom MCPs, custom logic in-silicon). Example: z1d instance with high-frequency CPU with all-core turbo of 4GHz.
*Semi-custom products: integrate IP of customers, use packaging technologies
*Deliver fully custom ASICs for customers
*See 2015 slide: https://www.fool.com/investing/general/2014/12/08/in-the-datacenter-intel-corporations-business-mode.aspx
*200 HW/SW engineers actively engaged with CSPs -> hand-on side by side engineering, working on 150 CSP projects in 2018
*Example: Tencent WeChat app (1B users) -> allows partner apps to run on top of it -> AI performance is critical -> worked with Tencent to increase performance 16x
*Cloud Insider program with 650+ members -> get access to Intel engineers and sandbox development for new hardware -> 30% revenue uplift from the engagements by designing balanced solution at the platform level
*>$100M/year on co-marketing: https://twitter.com/Ashraf__Eassa/status/1027251771209445377
*Summary: https://images.anandtech.com/doci/13192/1533750777348666280219.jpg
(posting this thread during the break... after the break, there's a talk about the enterprise, about networking and a Q&A... and maybe even more talks after lunch)
Videos and PDFs: http://intelstudios.edgesuite.net/180808_dcis/index.html
Intel Newsroom: https://newsroom.intel.com/editorials/data-centric-innovation-summit/
Navin Shenoy: Opening Keynote
*Will give update on roadmap and “how we will win”.
*~50% of revenue is from data-centric and growing double digits
*90% of world’s data created in last 2 years
*example: automotive -> example of end-to-end compute problem (AI, cloud, edge, networking)
*Intel TAM: forecast from IM2017: 2021 $160B data-centric TAM shared // forecast NOW: $200B data-centric https://images.anandtech.com/doci/13192/1533744704225791588519.jpg
*2/3 of cloud is TAM expansion (growing at 40% in H1’18)
*Cloud. 50% of CSP (cloud service provider) CPU volume is a custom CPU, up from 18% in 2013 (“highly customized products for their needs”)
*Networking: cloudification of network (core, access and edge). 20% MSS today of $24B 2022 silicon TAM. ISA optimizations, software, ecosystem, low-power hardware, etc. Example: 1 million base stations sold every year -> imagine Xeons in every one of them.
*AI. $2.5B market in 2017, growing 30% CAGR through 2022 to up to $10B -> investing heavily, biggest incremental investment of Intel
*DC network traffic (e.g. data moving from rack to rack): growing 25% CAGR -> connectivity becoming bottleneck -> full portfolio ($4B TAM growing to $11B in 2022): omni-path fabric, ethernet (#1 MSS, adding “SmartNIC” Cascade Glacier in 2019 combining FPGA and ethernet portfolio), and silicon photonics (Intel’s unique advantage: integrate laser in silicon)
*Moving data: Optane SSD and DIMMs (persistent memory) -> changing memory hierarchy. First QLC consumer and data center solution. Optane SSDs with 40x lower latency and higher endurance. Optane persistent memory.
*Optane persistent memory: $10B 2022 SAM (subset of 2022 DC DRAM market): big performance increase in certain workloads (8-11x). Persistency: from minutes to seconds of reboot times, from three 9s to five 9s of availability. A lot of work required, like software and “Revamp the IMC on the CPU”.
*Google: SAP HANA workload with persistent memory and Cascade Lake
*Announces first production modules of Optane PM shipped yesterday out of the factory -> Optane PM entering production!
*Congratulates engineers working on Optane PM, some working for over a decade to make it reality
*Process everything: Xeon, FPGA and Nervana ASIC.
*20th anniversary of Xeon CPU family -> industry leading processor for data center processing everywher: 220M Xeons, $130B revenue. Tailor Xeon for customer workloads and continue advancements. https://images.anandtech.com/doci/13192/15337460999211370399119.jpg
*Xeon Scalable stats: https://images.anandtech.com/doci/13192/1533746164839496564550.jpg.
*Performance leadership: up to 1.48x per core, 1.72x for L3 packet forwarding, 3.2x Linpack, 1.85x database, 1.45x memory caching
*Reinventing Xeon for AI: inference and training improved by >200x with hardware and software innovations from HSW to SKL
*Through software optimizations in last year: inference improved by 5.4x (INT8) since July’17
*2017: >$1B in revenue from Xeon AI revenue and big growth
*Next-gen: Cascade Lake. Ship in late 2018. New integrated memory controller for Optane PM. Spectre and Meltdown protections. New cache, new instructions.
*Adding new AI extensions: Intel Deep Learning Boost -> extends AVX-512 -> doubles performances (this is already in Knights Mill as VNNI instructions) https://images.anandtech.com/doci/13192/15337465541401312204600.jpg
*Google demo of 11x speed-up from SKX to CSX. A 10x improvement in one year through HW/SW is pretty good.
*Intel Select Solutions: verify configuration: engineer, validate and test solutions at HW and SW level -> makes easy to deploy complex solutions for faster TTM -> for analytics, network, HPC,… Announcement: new Select Solutions around AI, Blockchain and SAP HANA (Optane PM)
*The Intel differentiation: harness all of Intel’s assets (from transistor, architecture, memory, interconnect, security to software) -> welcome Jim Keller to stage, SVP of Engineering
*Came to Intel: amazing opportunity, scale, excellence of engineering, met many people, likes diversity of technology. Working at now: focus on 14nm and 10nm products coming to market, getting up to speed on the process, performance, yield.
*Roadmap talk
*Cooper Lake has “significantly better generation on generation improvement”.
*Navin: “Maintain our leadership position in the data center.”
Introduces Naveen Rao!
Naveen Rao: Unlocking Data Value with AI
*Want to make AI more broadly adopted
*Market: $2.5B in 2017, $8-10B in 2022: about evenly split between training/inference, but line between inference/training will become more blurred
*2012: 16.000 CPUs for 2 weeks proof of concept, now applying to real problems
*Intel’s heterogenous portfolio for AI from edge to data center, with shared IP portfolio https://images.anandtech.com/doci/13192/15337477631951431458216.jpg
*Reiterates AI is a $1B business for Intel today (only Xeon in data center, number doesn’t include FPGA and IoT)
*AI development lifecycle. 30% of development time is training, done by GPUs today. https://images.anandtech.com/doci/13192/1533748089180597043906.jpg
*AI roadmap. Debunks GPU myth. “Reality is almost all inference in the world runs on Xeon today”. Reiterates 5.4x inference performance since July’17 (and 1.4x for training), and 11x with Cascade Lake -> focus on lower precision (bfloat16 with Cooper Lake)
*NNP for inference: in development.
*NNP for training, coming in late 2019 NNP L-1000: bloat16, 3-4x performance of Lake Crest, high-bandwidth, low-latency interconnects. -> what really matters is performance (time to train) and efficiency https://images.anandtech.com/doci/13192/1533748403816342061948.jpg
*50% of AIPG (AI Products Group) is dedicated to software (libraries like MKL-DNN) -> but wants to make AI accessible to broad enterprise customers -> even higher level of abstraction for edge: OpenVINO toolkit and Movidius SDK https://images.anandtech.com/doci/13192/1533748664844850855111.jpg
*nGraph. Deep learning compiler nGraph: connects all hardware platforms (Xeon, Movidius, FPGA, GPU, etc.) to all software platforms (TensorFlow, etc.) https://images.anandtech.com/doci/13192/15337487256991267737489.jpg
*Example: Novartis (1028x1280x3) drug discovery: much higher resolution than Imagenet (224x224x3) -> >6x increase in performance by going from 1 to 8 CPUs (using omni-path), and 20x improvement with SW improvements
*Xeon has TCO benefit because it can be amortized over other things than just purely AI workload
*AI Builer Program with “vibrant ecosystem” https://images.anandtech.com/doci/13192/1533749058963243119428.jpg
*Open source community: NLP Architect, nGraph, Coach, Distiller
*AI Academy and DevCloud (110K developers, 150k users each month)
*Expanding AI DevCon (introduced in May)
RaeJeanne Skillern (GM of CSP Group): Cloud: Driving Transformation & Growth
*Three learnings: (1) One size does not fit all. (2) More than just better efficiency: differentiation. (3) Long-term partnerships at scale takes time.
*Cloud revenue stats (43% of revenue in H1’18): https://images.anandtech.com/doci/13192/15337495883751562984527.jpg
*Makes sure all of the Xeon compute capabilities are exploited by the CSPs
*In addition to standard roadmap: custom SKUs (>50% are custom). Xeon-D created out of joint innovation with Facebook. Nearly 30 off-roadmap, customized SKUs (for instance custom MCPs, custom logic in-silicon). Example: z1d instance with high-frequency CPU with all-core turbo of 4GHz.
*Semi-custom products: integrate IP of customers, use packaging technologies
*Deliver fully custom ASICs for customers
*See 2015 slide: https://www.fool.com/investing/general/2014/12/08/in-the-datacenter-intel-corporations-business-mode.aspx
*200 HW/SW engineers actively engaged with CSPs -> hand-on side by side engineering, working on 150 CSP projects in 2018
*Example: Tencent WeChat app (1B users) -> allows partner apps to run on top of it -> AI performance is critical -> worked with Tencent to increase performance 16x
*Cloud Insider program with 650+ members -> get access to Intel engineers and sandbox development for new hardware -> 30% revenue uplift from the engagements by designing balanced solution at the platform level
*>$100M/year on co-marketing: https://twitter.com/Ashraf__Eassa/status/1027251771209445377
*Summary: https://images.anandtech.com/doci/13192/1533750777348666280219.jpg
(posting this thread during the break... after the break, there's a talk about the enterprise, about networking and a Q&A... and maybe even more talks after lunch)