Service providers and end users worldwide are seeing the benefits of artificial intelligence (AI) as machine learning algorithms are increasingly used to process the world’s data and enhance our digital services. Using AI to make the most of the data opportunity requires a complete workflow, from data science workstations up to cloud and eventually out to inference devices – not only for processing data, but moving and storing data as well.
Today, we’re moving from training models to deploying them in the real world. Previously, much of the work in AI focused on training – refining the model for the application you hope to render. However, end users for AI services don’t experience training. They experience inference – the rendering of the AI service.
Services often must render results rapidly to be relevant to their end users – whether those are medical professionals, research scientists, or consumers of voice recognition services. As a result, we see more inference in local servers and Internet of Things (IoT) devices at the edge, driven by the need for low-latency, real-time inference results, in addition to the inference on less-time-sensitive data sent the cloud.
At Data-Centric Innovation Day, we are excited to highlight several 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.
The Texas Advanced Computing Center (TACC) will use Intel® Xeon® Platinum 8200 processors to power its Frontera system, supporting multi-faceted advanced research for the National Science Foundation. At Data-Centric Innovation Day, we are excited to share that Frontera will also incorporate more than 100 terabytes of Intel Optane DC persistent memory, the first installation of the technology at this scale. This store of persistent memory in close proximity to performant compute will enable simulations, AI algorithms, and in-memory analytics of unprecedented complexity. Frontera will help to reveal what’s possible with massively-parallel AI inference on high-performance computing systems. We eagerly look forward to the discoveries that Frontera will produce.
iFLYTEK is one of the most innovative companies in the People’s Republic of China and supports a variety of voice-based products in industries like communications, music, and intelligent toys. Customers will turn elsewhere if the company can’t process its daily volume of six billion voice recognition transactions expediently. iFLYTEK faces the continual challenge of expanding data center capacity to keep up with increasing customer demand, with total cost of ownership as one of their primary concerns. Adding to this challenge is iFLYTEK’s ongoing expansion into new businesses such as education and medical diagnostics.
For several years, iFLYTEK has actively migrated more of its business to Intel architecture, including 2nd Gen Intel Xeon Scalable processors with Intel® Deep Learning Boost (Intel DL Boost). The AI giant’s reliance on Intel reiterates the capabilities of Intel solutions to deliver leading AI products in a cost-effective manner to end users.
Data-Centric Innovation Day features the debuts of technologies that will be under the hood of systems running complex AI workloads alongside the traditional data center and cloud applications at which Intel Xeon Scalable processor based systems excel.
The results customers are realizing with versatile, efficient, performant Intel architecture, especially when dealing with very large workloads, demonstrate again and again that real-world AI solutions require systems able to balance the need to move, process, and store larger and larger quantities of data. It’s not only about whether you have the right processors, accelerators, and storage. It’s how you balance the entire system between compute, acceleration, memory, memory access, and interconnect.
Ultimately, AI service providers will succeed or fail based on the quality of experience provided to their own end customers. In use cases like those discussed here, the speed of AI inference and accuracy of results delivered will determine whether a product is relevant or irrelevant to end users. The products Intel announced at Data-Centric Innovation Day will enable complete AI systems for scalable, deployable, real-world results at the speeds end users require. For more on Intel’s product portfolio for AI, please visit www.intel.ai.
 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“.
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