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GPUs and other co-processors will provide strong boost to Artificial Intelligence

19 June 2019 Tom Hackenberg

Artificial intelligence (AI) may seem to be a fashionable buzzword these days, but the term was first coined at Dartmouth College in New Hampshire more than 50 years ago. Since then, AI has evolved considerably from its rarefied academic beginnings.

Generally understood to be the animating technology by which machines mimic the cognitive functions normally associated with a human mind to resemble human intelligence, AI is increasingly viewed as a significant force of change for the future, part of an elite group of so-called transformative technologies including 5G and IoT, which will fundamentally alter the way people live their lives, conduct business, and make things.

It is common to find AI functionality today in servers as well as high-performance computers because high processing power has traditionally been required to conduct AI applications, especially in the training aspect of the application. But the vast changes now taking place in AI started approximately a decade ago, driven by developments at US graphics technology leader Nvidia that sought to harness the power of GPUs for AI-related tasks.

The use of GPUs produced electrifying results, delivering a stunning increase of anywhere from 10 to 100 times the performance of AI-related tasks compared to what could be obtained from a microprocessor (MPU).


Scalar processors vs. vector processors

Scalar processors are those that perform computations on one or a small set of data at a time. One common scalar processor is the MPU, which performs the functions of a central processing unit in computers and similar high-performance compute devices.

Another common scalar processor—but less well-known—is the microcontroller (MCU). MCUs are deployed in everything from the smallest handheld devices to cars and industrial automation. The scalar design in the MCU is well suited to the flexibility required to run a variety of applications, respond quickly to the human interface, and manage resources—with all the tasks executed simultaneously. Historically, it has been the raw performance and speed of the MPU that has enabled it to perform AI tasks in data centers and high-performance computers, but scalar architecture is not optimized for the kind of vector or matrix math that is commonly used for AI.

In comparison, GPUs are specialized processors highly efficient at handling computer graphics and image processing. With many smaller more optimized cores and direct access to manipulating data in memory, GPUs excel at vector math. Through the rapid manipulation of a computer's hardware memory, GPUs accelerate the rendering of images in the memory buffer, with the resulting images then output in lightning speed to a computer monitor, TV screen, or similar display device. It is this same vector processing, now applied to general-purpose applications, that enables AI applications to be offloaded from the primary applications processor to a GPU for general-purpose vector processing at much greater efficiency.


AI joins the mainstream

With the discovery that AI performance could be enhanced significantly by deploying GPUs, artificial intelligence took on fresh urgency and expanded quickly. From the cozy enclaves of academia and the opaque halls of the defense establishment, AI found its way into private enterprise and industry. Today, the technology is in mainstream applications—from your browser search engine, to natural language processing in digital assistants such as Amazon's Alexa, Google's Assistant, Apple's Siri, Microsoft's Cortana, and Samsung's Bixby. New AI applications are also starting to permeate the markets for transformative and emerging technologies.

At present, AI capabilities are carried out mainly by MPUs in a datacenter environment. And among servers today, less than 3% contain any co-processors that ideally could be used to host AI functionality and significantly boost AI capabilities. Instead, AI today remains firmly in the charge of the MPU.

That will all soon change, however. Within five years, AI functionality will expand greatly from the microprocessor domain, and servers will incorporate platforms and co-processors. Platforms will include GPUs and AI accelerators from suppliers like AMD, Intel, Nvidia, and Xilinx; discrete AI processors like those being made by Amazon, Google, Microsoft and several emerging suppliers; and system-on-chips (SoC) with integrated machine learning (ML) capabilities.

By 2023, servers with AI-bearing co-processors will represent up to 15% of global server shipments, IHS Markit is forecasting. Projections also show that the percentage of processors optimizing AI functionality will rise substantially, growing by as much as 100% annually for the next several years.

Even more importantly, processors with AI optimization will be increasingly deployed in a variety of embedded mechanisms, such as those used in security cameras and in advanced driver assistance systems (ADAS) for cars, as well as in applications where image analysis—for purposes of security and identification, for instance—will be of paramount importance. Applications of AI will be able to run inferencing, using AI trained programs to analyze patterns of data to make logical conclusions, in a very wide variety of applications, utilizing the smallest of AI-optimized SoCs and ML processors to completely transform our world.


Tom Hackenberg is associate director and senior principal analyst for processors at IHS Markit
Posted 19 June 2019

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