Intel VP talks AI strategy as company takes on Nvidia
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Intel is on an artificial intelligence (AI) mission that it considers very, very possible.
The company is the world’s largest semiconductor chipmaker by revenue and is best known for dominating the processor market, with its familiar “Intel inside” campaign – reminding us all of what was inside our personal computers. However, in an age when AI chips are all the rage, the company finds itself chasing competitors, including Nvidia, which has a massive head start in processing AI with its GPUs.
There are significant benefits to catching up in this space. According to a report, the AI chip market was worth around $8 billion in 2020, but is expected to reach nearly $200 billion by 2030.
At Intel’s Vision event in May, the company’s new CEO, Pat Gelsinger, emphasized that AI is at the heart of the company’s future products, while predicting that the need for AI for higher compute performance levels makes it a key driver of Intel’s overall strategy.
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Gelsinger said he envisions four superpowers driving innovation at Intel: ubiquitous connectivity, ubiquitous computing, AI, and cloud-to-edge infrastructure.
This requires high-performance hardware and software systems, including in the tools and frameworks used to implement end-to-end AI and data pipelines. Accordingly, Intel’s strategy is to “build a stable of chips and open-source software that covers a wide range of computing needs as AI becomes more prevalent”, recently the wall street journal noted item.
“Each of these superpowers is impressive on its own, but when they come together it’s magical,” Geisinger said at the Vision event. “If you don’t apply AI to every one of your business processes, you’re falling behind. We see it in all sectors. »
It is in this context that VentureBeat recently spoke with Wei Li, vice president and general manager of AI and analytics at Intel. He is responsible for AI software and hardware acceleration and analytics for deep learning, machine learning and big data analytics on Intel CPUs, GPUs, AI accelerators and XPUs with heterogeneous and distributed computing.
Intel Software and Hardware Connection
According to Li, it’s Intel’s close connection between software and hardware that makes the company stand out and ready to compete in the AI space.
“The biggest problem we’re trying to solve is to bridge data and information,” he said. “The bridge needs to be wide enough to handle a lot of traffic, and the traffic needs to have speed and not get stuck.”
This means that AI needs software to work efficiently and quickly, with a comprehensive ecosystem that allows data scientists to take large amounts of data and design solutions, and hardware acceleration that offers the ability to process data efficiently.
“On the hardware side, when we add specific acceleration inside the hardware, we need to know what we’re accelerating,” Li said. “So we do a lot of co-design, where the software team works very closely with the hardware team.”
The two groups work almost like a single team, he added, to understand patterns, uncover performance bottlenecks and add hardware capacity.
“It’s an outside-in approach, tightly integrated co-design, to make sure the hardware is designed the right way,” he said, adding that the stock GPU wasn’t designed for AI, but had the right amount of compute and bandwidth. Since then, GPUs have evolved.
“When we design GPUs these days, we see AI as an important workload to drive GPU design,” he said. “There are specific features inside the GPU that are purely for AI. That’s the advantage of being in a company where we have both software and hardware teams.
Intel’s goal is to expand its AI efforts, Li said, which he said aims to develop an ecosystem rather than separate solutions.
“This will be how we lead and nurture an open AI software ecosystem,” he explained. “Intel has always been an open ecosystem that enables competition, which allows Intel’s technologies to be brought to market faster at scale.”
Intel’s Trained AI Benchmark Kits Boost Speed
Historically, Intel has worked hard on software capability to achieve better performance, essentially increasing the width of the bridge between data and information.
Last month, Intel released trained AI benchmark kits for the open-source community, which Li says is one of the steps the company is taking to increase bridge-crossing speed.
“Traditionally, AI software was designed for most specialists,” he said. “But we want to target a much broader set of developers.”
Reference Kit AI models have been designed, trained, and tested against thousands of models for specific use cases, while data scientists can customize and fine-tune the model with their own data.
“You get a combination of ease of use because you’re starting from something almost pre-cooked, and you get all the optimized software as part of the package so you can get your fix quickly,” Li explained.
Priorities for next year
One of Intel’s biggest AI priorities in the coming year is software.
“We will put more effort into ease of use,” Li said.
On the hardware side, he added, new products will focus heavily on performance, including the Sapphire Rapids Xeon server processor which will be released in 2023.
“It’s like a CPU with an integrated GPU inside because of the amount of compute capacity you have,” Li said. “It’s a game-changer to have all the acceleration inside the GPUs.”
Additionally, Intel is focusing on the performance of its data center GPU, working with its customer Argonne National Laboratory, which serves its customers and developers.
Intel AI’s Biggest Challenges
Li said the biggest challenge facing his team is executing Intel’s AI vision.
“We really want to make sure we’re running well so we can stick to the right schedule and make sure we’re running fast,” he said. “We want to have a scorching pace, which is not easy for a big company.”
However, Li won’t blame external factors creating challenges for Intel, such as the economy or inflation.
“Everyone has headwinds, but I want to make sure we’re doing the best we can with the things we control as a team,” he said. “So I’m pretty optimistic, especially in the area of AI. It’s like going back to my graduate student days – you can really think big. Everything is possible.”
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