
Using a Global AI Platform to Use AI Everywhere
Companies all across the world use artificial intelligence every day to speed up scientific advancement and transform consumer and commercial services.
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CIO Applications Europe | Tuesday, February 21, 2023

Companies leverage artificial intelligence to accelerate scientific discovery, and transform consumer and business services.
FREMONT, CA: Companies all across the world use artificial intelligence every day to speed up scientific advancement and transform consumer and commercial services. Unfortunately, not all companies are using AI equally. The State of AI in 2022 research, adoption of AI by businesses has plateaued at 50 per cent. Leaders in AI are edging out the competition. One explanation is that 53 per cent of AI projects never reach production.
It is a moment to look at the hurdles of moving from concept to deployment because the benefits of AI to everyone are tremendous and the problems with AI being in the hands of only a few are too worrying.
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Despite the focus on the performance of deep learning models, AI is not a standard.
For data scientists, AI begins as a complete (E2E) pipeline that includes data engineering, testing, and live data streaming utilising both traditional (ML) and deep learning (DL) methods. To run any AI code at its best, the E2E pipeline needs a platform with a balance of memory, throughput, dense matrix, and general-purpose computing.
AI is a capability that, for the line of business, improves an application and conforms with service levels (SLA), such as throughput, latency, and platform flexibility. Because projects are waterfalled from the data team to model development, to the team operationalizing from the data centre to the manufacturing floor, projects have trouble moving from experimental to production. Rework results from these actions are often completed on various platforms. A collaborative approach incorporates a single E2E platform architecture from the data centre to the edge and pushes the production SLA requirements upstream.
Any AI code can be run on a global AI platform, which also can enable every developer the flexibility to enable AI everywhere. By providing end-to-end application performance rather than DL or ML kernel performance, Intel's objective is to speed up the integration of AI into every application. The entire stack—from chips to software libraries to applications—must be tuned to scale AI.
The 3 Components of a Universal AI Platform Are
General Purpose and AI-Specific Compute: By combining the adaptability of a general-purpose CPU with the power of a deep learning accelerator, fourth generation Intel® Xeon® Scalable processors can execute any AI code and handle any task. Additionally, the CPUs are easily able to connect with other processors and specialised accelerators like GPUs and ASICs.
Open-source Frameworks: AI models and E2E optimization tools are part of an AI software package with open standards that allow developers to create and use AI everywhere.
Ecosystem Participation: Pre-built solutions with Intel partners to meet the business demands of the end client and shorten the time to market.
Run any AI code and Every Workload
AI on CPUs has the benefits of accessibility, adaptability, and familiarity with programming models. With the performance of an AI accelerator built-in, 4th Generation Intel® Xeon® Scalable processors are balanced for the best performance across workloads. Given their irregular and sparse nature compared to deep learning's small-matrix-dense-algebra, classical machine learning and generic compute functions for ingestion are more difficult in the E2E pipeline.
For many AI applications, the data and machine learning stages take up the majority of the computation cycles, and they currently function effectively on Intel Xeon® processors. The BF16 and INT8 matrix multiplication engines from Intel's (NASDAQ: INTC) Advanced Matrix Extensions (Intel® AMX) have been incorporated into every core to support deep learning.
It improves upon vector extensions found in earlier Xeon® processor generations and offers up to a 10x increase in generation-to-generation inferencing and training model performance while requiring no code changes or the use of any framework.
Additionally, compared to AMD, Intel achieves 1.9X6 greater performance for conventional machine learning thanks to current software optimizations and improvements in computing, memory, and bandwidth.
With its capacity to speed up the End-to-End AI pipeline, the 4th generation Intel® Xeon® Scalable Processor is the appropriate basis for a global AI platform.
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