Intel invested $16.7 billion when it bought FPGA maker Altera in 2016, while AMD spent upwards of $35 billion in 2020 to acquire Xilinx, and but the vendors have little if anything to show for it.
For the fourth quarter, AMD’s embedded segment (which is where the FPGA business is slotted) revenue was $923 million, down 13% year-over-year, while for the year, segment revenue was $3.6 billion, down 33% from the prior year.
Intel didn’t fare much better. For the fourth quarter, Altera delivered revenue of $429 million, up 4% sequentially. For Q1, it expects Altera revenue to be down sequentially.
Not a great return on investment for either company.
FPGAs, notable because they can be reprogrammed for new processing tasks, seem to have lost their luster in the mania around generative AI. GPUs are all the rage, or in some cases, custom silicon specifically designed for inferencing.
So where does that leave the FPGA? On the side of the road, it would seem. Intel spun off the Altera business unit as a separate company while AMD didn’t discuss FPGA on its most recent earnings call.
Part of the problem is that they are one trick pony. Both Intel and AMD use their FPGAs for high-end networking cards. “I see these things are basically really powerful networking cards and nothing more or very little beyond that,” said Alvin Nguyen, senior analyst with Forrester Research.
“I think AI and GenAI helped kind of push away focus from leveraging [FPGA]. And I think there were already moves away from it prior to [the GenAI revolution], that put the pedal to the metal in terms of not looking at the FPGAs at the high end. I think now it’s [all about] DeepSeek and is kind of a nice reset moment,” he added.
One of the things about the recent news around DeepSeek AI that rattled Wall Street so hard is the Chinese company achieved performance comparable to ChatGPT and Google Gemini but without the billions of dollars’ worth of Nvidia chips. It was done using commercial, consumer grade cards that were considerably cheaper than their data center counterparts.
That means all might not be lost when it comes to FPGA.
“After DeepSeek showing that you could use lower power devices were more commonly available, [FPGA] might be valuable again,” said Nguyen. But he adds “It’s not going to be valuable for all AI workloads like the LLMs, where you need as much memory, as much network bandwidth, as much compute, in terms of GPU as possible.”
So Nguyen feels that DeepSeek show you don’t necessarily need billions of dollars of cutting-edge Nvidia GPUs, you can get away with an FPGA, a CPU, or use consumer grade GPUs. “I think that’s kind of a nice ‘aha’ moment from an AI perspective, to show there’s a new low bar that’s being set. If you can throw CPUs with a bunch of memory, or, in this case, if you can look at FPGAs and get something very purpose built, you can get a cluster of them at lower cost.”
But Bob O’Donnell, president and chief analyst with TECHpinions, disagrees with the comparison. “FPGAs are used in a whole bunch of different applications, and they’re not really a one-to-one compare against GPUs. They’re kind of a different animal,” he said.
The problem with FPGAs has always been they’re extraordinarily hard to program and they’re extremely specialized. So there are very few people who really know how to leverage these things, but for the people who do, there’s no replacement, and they’re not typically used for the same kinds of tasks that GPUs are used for, he said.
The jury is still out on whether Intel that its money’s worth, but O’Donnell feel that AMD did because AMD’s neural processing unit (NPU) for AI acceleration in its CPUs comes from Xilinx technology.
”That was the idea, to take some of the IP that Xilinx had and help integrate it into a PC. In fact, AMD was the first have any kind of NPU. They were way ahead of the game,” he said.
O’Donnell said it’s still up to for debate whether DeepSeek’s claims of using low-end hardware are actually true. “But that it’s fair to say that it raised the conversation of, yes, you can run powerful models on much less hardware than we were told we needed,” O’Donnell said.
Is that an opportunity for FPGA? “I don’t know that it is, though, because the bigger problem with all of this stuff is running is the software that runs on those GPUs,” he said. “It’s all about the software, not the actual chips. And there’s no equivalent that I’m aware of at all that lets you take these big models and run them on FPGAs.”
So can FPGAs fit in to this brave new world of generative AI? The jury is still out.
Source:: Network World