Tech
The FPGA Chip Is an IEEE Milestone
Many of the world’s most advanced electronic systems—including Internet routers, wireless base stations, medical imaging scanners, and some artificial intelligence tools—depend on field-programmable gate arrays. Computer chips with internal hardware circuits, the FPGAs can be reconfigured after manufacturing.
On 12 March, an IEEE Milestone plaque recognizing the first FPGA was dedicated at the Advanced Micro Devices campus in San Jose, Calif., the former Xilinx headquarters and the birthplace of the technology.
The FPGA earned the Milestone designation because it introduced iteration to semiconductor design. Engineers could redesign hardware repeatedly without fabricating a new chip, dramatically reducing development risk and enabling faster innovation at a time when semiconductor costs were rising rapidly.
The ceremony, which was organized by the IEEE Santa Clara Valley Section, brought together professionals from across the semiconductor industry and IEEE leadership. Speakers at the event included Stephen Trimberger, an IEEE and ACM Fellowwhose technical contributions helped shape modern FPGA architecture. Trimberger reflected on how the invention enabled software-programmable hardware.
Solving computing’s flexibility-performance tradeoff
FPGAs emerged in the 1980s to address a core limitation in computing. A microprocessor executes software instructions sequentially, making it flexible but sometimes too slow for workloads requiring many operations at once.
At the other extreme, application-specific integrated circuits are chips designed to do only one task. ASICs achieve high efficiency but require lengthy development cycles and nonrecurring engineering costs, which are large, upfront investments. Expenses include designing the chip and preparing it for manufacturing—a process that involves creating detailed layouts, building masks for the fabrication machines, and setting up production lines to handle the tiny circuits.
“ASICs can deliver the best performance, but the development cycle is long and the nonrecurring engineering cost can be very high,” says Jason Cong, an IEEE Fellow and professor of computer science at the University of California, Los Angeles. “FPGAs provide a sweet spot between processors and custom silicon.”
Cong’s foundational work in FPGA design automation and high-level synthesis transformed how reconfigurable systems are programmed. He developed synthesis tools that translate C/C++ into hardware designs, for example.
At the heart of his work is an underlying principle first espoused by electrical engineer Ross Freeman: By configuring hardware using programmable memory embedded inside the chip, FPGAs combine hardware-level speed with the adaptability traditionally associated with software.
The FPGA architecture originated in the mid-1980s at Xilinx, a Silicon Valley company founded in 1984. The invention is widely credited to Freeman, a Xilinx cofounder and the startup’s CTO. He envisioned a chip with circuitry that could be configured after fabrication rather than fixed permanently during creation.
Articles about the history of the FPGA emphasize that he saw it as a deliberate break from conventional chip design.
At the time, semiconductor engineers treated transistors as scarce resources. Custom chips were carefully optimized so that nearly every transistor served a specific purpose.
Freeman proposed a different approach. He figured Moore’s Law would soon change chip economics. The principle holds that transistor counts roughly double every two years, making computing cheaper and more powerful. Freeman posited that as transistors became abundant, flexibility would matter more than perfect efficiency.
He envisioned a device composed of programmable logic blocks connected through configurable routing—a chip filled with what he described as “open gates,” ready to be defined by users after manufacturing. Instead of fixing hardware in silicon permanently, engineers could configure and reconfigure circuits as requirements evolved.
Freeman sometimes compared the concept to a blank cassette tape: Manufacturers would supply the medium, while engineers determined its function. The analogy captured a profound shift in who controls the technology, shifting hardware design flexibility from chip fabrication facilities to the system designers themselves.
In 1985 Xilinx introduced the first FPGA for commercial sale: the XC2064. The device contained 64 configurable logic blocks—small digital circuits capable of performing logical operations—arranged in an 8-by-8 grid. Programmable routing channels allowed engineers to define how signals moved between blocks, effectively wiring a custom circuit with software.
Fabricated using a 2-micrometer process (meaning that 2 µm was the minimum size of the features that could be patterned onto silicon using photolithography), the XC2064 implemented a few thousand logic gates. Modern FPGAs can contain hundreds of millions of gates, enabling vastly more complex designs. Yet the XC2064 established a design workflow still used today: Engineers describe the hardware behavior digitally and then “compile the design,” a process that automatically translates the plans into the instructions the FPGA needs to set its logic blocks and wiring, according to AMD. Engineers then load that configuration onto the chip.
The breakthrough: hardware defined by memory
Earlier programmable logic devices, such as erasable programmable read-only memory, or EPROM, allowed limited customization but relied on largely fixed wiring structures that did not scale well as circuits grew more complex, Cong says.
FPGAs introduced programmable interconnects—networks of electronic switches controlled by memory cells distributed across the chip. When powered on, the device loads a bitstream configuration file that determines how its internal circuits behave.
“As process technology improved and transistor counts increased, the cost of programmability became much less significant,” Cong says.
From “glue logic” to essential infrastructure
“Initially, FPGAs were used as what engineers called glue logic,” Cong says.
Glue logic refers to simple circuits that connect processors, memory, and peripheral devices so the system works reliably, according to PC Magazine. In other words, it “glues” different components together, especially when interfaces change frequently.
Early adopters recognized the advantage of hardware that could adapt as standards evolved. In “The History, Status, and Future of FPGAs,” published in Communications of the ACM, engineers at Xilinx and organizations such as Bell Labs, Fairchild Semiconductor, IBM, and Sun Microsystems said the earliest uses of FPGAs were for prototyping ASICs. They also used it for validating complex systems by running their software before fabrication, allowing the companies to deploy specialized products manufactured in modest volumes.
Those uses revealed a broader shift: Hardware no longer needed to remain fixed once deployed.
Attendees at the Milestone plaque dedication ceremony included (seated L to R) 2025 IEEE President Kathleen Kramer, 2024 IEEE President Tom Coughlin, and Santa Clara Valley Section Milestones Chair Brian Berg.Douglas Peck/AMD
Semiconductor economics changed the equation
The rise of FPGAs closely followed changes in semiconductor economics, Cong says.
Developing a custom chip requires a large upfront investment before production begins. As fabrication costs increased, products had to ship in large quantities to make ASIC development economically viable, according to a post published by AnySilicon.
FPGAs allowed designers to move forward without that larger monetary commitment.
ASIC development typically requires 18 to 24 months from conception to silicon, while FPGA implementations often can be completed within three to six months using modern design tools, Cong says. The shorter cycle and the ability to reconfigure the hardware enabled startups, universities, and equipment manufacturers to experiment with advanced architectures that were previously accessible mainly to large chip companies.
Lookup tables and the rise of reconfigurable computing
A popular technique for implementing mathematical functions in hardware isthe lookup table (LUT). A LUT is a small memory element that stores the results of logical operations, according to “LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs,” a paper selected for presentation next month at the 34th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM).
Instead of repeatedly recalculating outcomes, the chip retrieves answers directly from memory. Cong compares the approach to consulting multiplication tables rather than recomputing the arithmetic each time.
Research led by Cong and others helped develop efficient methods for mapping digital circuits onto LUT-based architectures, shaping routing and layout strategies used in modern devices.
As transistor budgets expanded, FPGA vendors integrated memory blocks, digital signal-processing units, high-speed communication interfaces, cryptographic engines, and embedded processors, transforming the devices into versatile computing platforms.
Why the gate arrays are distinct from CPUs, GPUs, and ASICs
FPGAs coexist with other processors because each one optimizes different priorities. Central processing units excel at general computing. Graphics processing units, designed to perform many calculations simultaneously, dominate large parallel workloads such as AI training. ASICs provide maximum efficiency when designs remain stable and production volumes are high.
“ASICs can deliver the best performance, but the development cycle is long, and the nonrecurring engineering cost can be very high. FPGAs provide a sweet spot between processors and custom silicon.” —Jason Cong, IEEE Fellow and professor of computer science at UCLA.
“FPGAs are not replacements for CPUs or GPUs,” Cong says. “They complement those processors in heterogeneous computing systems.”
Modern computing platforms increasingly combine multiple types of processors to balance flexibility, performance, and energy efficiency.
A Milestone for an idea, not just a device
This IEEE Milestone recognizes more than a successful semiconductor product. It also acknowledges a shift in how engineers innovate.
Reconfigurable hardware allows designers to test ideas quickly, refine architectures, and deploy systems while standards and markets evolve.
“Without FPGAs,” Cong says, “the pace of hardware innovation would likely be much slower.”
Four decades after the first FPGA appeared, the technology’s enduring legacy reflects Freeman’s insight: Hardware did not need to remain fixed. By accepting a small amount of unused silicon in exchange for adaptability, engineers transformed chips from static products into platforms for continuous experimentation—turning silicon itself into a medium engineers could rewrite.
Among those who attended the Milestone ceremony were 2025 IEEE President Kathleen Kramer; 2024 IEEE President Tom Coughlin; Avery Lu, chair of the IEEE Santa Clara Valley Section; and Brian Berg, history and milestones chair of IEEE Region 6. They joined AMD’s chief executive, Lisa Su, and Salil Raje, senior vice president and general manager of adaptive and embedded computing at AMD.
The IEEE Milestone plaque honoring the field-programmable gate array reads:
“The FPGA is an integrated circuit with user-programmable Boolean logic functions and interconnects. FPGA inventor Ross Freeman cofounded Xilinx to productize his 1984 invention, and in 1985 the XC2064 was introduced with 64 programmable 4-input logic functions. Xilinx’s FPGAs helped accelerate a dramatic industry shift wherein ‘fabless’ companies could use software tools to design hardware while engaging ‘foundry’ companies to handle the capital-intensive task of manufacturing the software-defined hardware.”
Administered by the IEEE History Center and supported by donors, the IEEE Milestone program recognizes outstanding technical developments worldwide that are at least 25 years old.
Check out Spectrum’s History of Technology channel to read more stories about key engineering achievements.
From Your Site Articles
Related Articles Around the Web
>
After more than a decade building a caller ID business serving over 500 million users, Truecaller is now taking the scam intelligence it gathered along the way to the open web, with no app or sign-in required.
The Swedish company on Sunday launched Scam Checker, a new web and Android service that lets users paste in a suspicious phone number, link, or message to find out if it’s fraudulent. To work, the service surfaces related reports from Truecaller’s community, ScamFeed. However, more detailed information about a phone number, including the name associated with it, remains available only through Truecaller’s existing service, which requires a sign-in.
The free-to-access tool will initially be available in India and is set to expand to Latin America, the Middle East and Africa, and Southeast Asia, the company said.
In addition to serving as lead generation for its app, the community reports can give Truecaller a better view of the scams circulating at a given time. This could also help the company with its fraud and risk products sold to enterprises through Truecaller for Business, although Jhunjhunwala said Scam Checker itself is aimed at consumers.
To work, Truecaller’s Scam Checker checks the link the user submits, expanding shortened URLs and following redirects to the final destination. It then checks these against its proprietary risk database and other fraud signals. Users can also paste a suspicious message, allowing the service to pick out a phone number or link and surface related reports from the Truecaller community.
The launch comes as scams have grown well beyond phone calls to text messages, messaging apps, and web links. In a 2025 GSMA survey of Indian adults (PDF), 46% of those who reported being scammed said they were approached through messaging apps, while 37% via SMS and 32% through voice calls.
Truecaller estimates that people make about 14 million web searches a month to check suspicious links and phone numbers, based on its analysis of search volumes and traffic to existing verification services. That behavior helped shape Scam Checker, CEO Rishit Jhunjhunwala told TechCrunch.
“When you need it, you’re usually somewhere else. The link shows up on WhatsApp. Your mum gets a message about a traffic fine. A friend forwards you a screenshot and asks, ‘Is this real?’” Jhunjhunwala said. “What people do in that moment is search.”
Truecaller’s community is becoming a crucial piece of its scam-detection effort. The company told TechCrunch that about 20,000 scam reports are live on ScamFeed, its crowdsourced feed where users can post and discuss scams, in India, with around 1,300 new reports added each week. Between September 14 and 20, the company also said it evaluated 12.9 billion messages globally and flagged 20.3 million as fraudulent.
In the near future, Truecaller says it plans to broaden the types of scams Scam Checker can detect and add screenshot uploads for further analysis.
The company’s push beyond caller ID comes as its core business faces new pressures in India, its largest market with more than 350 million users. Telecom operators are rolling out the federal government-backed Calling Name Presentation service, while Apple and Google have added their own caller identification and spam-protection features. Last week, India’s telecom regulator also ordered caller-ID apps to share user-submitted spam reports with telecom operators, a move Truecaller criticized as a “one-way exchange.”
As a result, Truecaller’s focus is evolving beyond caller ID.
“Caller ID was the first problem we solved, and it’s still how most people find us,” he said. “But scams moved to a more multi-channel approach with links and messages, and increasingly to voice and video. Our protection has to follow the scammer.”
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
>
Anthropic CEO Dario Amodei seems to be everywhere this weekend: He was lampooned on the season premiere of Saturday Night Live, and tonight, he’s set to have dinner with President Donald Trump at the White House.
Axios first broke the news of Amodei’s dinner plans, which were subsequently confirmed by other publications.
This will be the first one-on-one meeting between the two men, who recently found themselves on opposite sides of the AI safety debate. Amodei released a plan to slow AI development (or at least proceed with more caution), while Trump has insisted, without evidence, that the AI backlash is a Democratic hoax; he also wants to rebrand the technology as “super intelligence.”
Even before the current back-and-forth, Amodei and Anthropic have to had a fraught relationship with Trump’s administration. Earlier this year, the Pentagon designated Anthropic a supply-chain risk in response to the company’s attempt to put guardrails around the use of its technology (Anthropic has been fighting the designation in court), although other administration officials have been friendlier.
>
Meta’s new AI agent Muse took the spotlight at the company’s annual Connect event, where CEO Mark Zuckerberg made it clear that Facebook’s parent company plans to push AI features everywhere.
On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I discussed Meta’s AI announcements seemed to steal the spotlight during a week of new model launches from OpenAI and Anthropic.
With other big AI companies focused on coding and enterprise tools, it was a little surprising to see Meta move in the opposite direction, with a consumer focus and a cute, Tamagotchi-style AI device that Meta insists is for adults only. But as Kirsten noted, this could be playing to Meta’s strengths.
Sean tried Muse for himself, and while he was pleased that the agent actually found him some unclaimed money, he described the feature as more “a party-trick type thing,” rather than something that will drive ongoing usage. Plus, there’s the question of whether users can trust Meta’s AI with sensitive information.
“Meta’s business is to sell you ads,” Sean said. “And yes, they’ll make the argument that the more they know about you, the more accurate and interesting the ads will be — wake me up when we get to that fever dream.”
Keep reading for a preview of our full conversation, edited for length and clarity.
Kirsten Korosec: So how do you put Muse, which is this new personal AI agent that’s just been released by Meta and [is] clearly a bet on consumer — how does that fit into what you just described, at least with other frontier AI model companies seeing opportunity and business within enterprise? Because Meta Connect, which is their big annual event, just happened, and they are all-in on Muse, that is very clear.
Anthony Ha: That was definitely very head spinning for me, because it certainly feels like what we’ve been talking about has been this shift towards enterprise — not exclusively, but certainly that’s where the money, the attention is going.
Maybe some of that is because of the relative position of these different companies — OpenAI and Anthropic are in the lead in a lot of ways, but also, they’re planning to go public either this year, or next year in the case of OpenAI. And so there’s this feeling of, “I think we’ve got to actually make money now.” Not to say that they’re not making money [already], but because the costs are so high and the valuations are so high, they have to make money on this scale that’s essentially unprecedented. And I think they’re seeing enterprise as the way to do that.
And I wonder if Meta, for a variety of reasons, sees a different opportunity. There’s a part of me that’s like, “Wait, did they not get the memo?” But I think more charitably, you could say, “Well, if that’s where OpenAI and Anthropic are going, then maybe there is more of an opportunity for Meta to make the more consumer-friendly [version and] continue advancing AI on the consumer side.”
Kirsten: I mean, we can complain about or criticize or critique Meta all day long, but they’re very good and have [an] established track record of embedding themselves in everyday people’s lives. I mean, there’s a reason why Facebook has so many users — Instagram, WhatsApp. And I’ve never really thought of them as an enterprise product anyway. So I think it’s smart for them to continue to push on the consumer piece.
Sean, you’ve already tried Muse, which has already been out for a couple weeks. And I’m wondering if you see what your impression is, and if you see it being successful in the bid to become part of every part of your life.
Sean O’Kane: I mean, not really. I understand why some people think that is going to be the case. I’m sure a lot of people understand this, but this is roughly Meta’s kind ground-up version of an on-your iPhone, or on your Android, app of OpenClaw, which we talked about a couple months ago, which Meta went out and basically bought and integrated those folks’ work. It was the first big explosion of like, “Holy smokes, these agents can do all this stuff for me while I’m out and about, and I can just text with it and let it control my whole computer.” There’s a lot of the same elements of that at play. And having it in your hand, on an app that works like a relatively good chatbot as the interface, it does seem pretty powerful.
One of the first things that I did with it was — because it makes a bunch of suggestions for you, as to things that it can do, and one of them was, “I’ll scan to see if you have any unclaimed funds,” this thing that I think no one ever really thinks about and often is going to completely miss, because there’s just not a lot of unclaimed property funds out there in your name. Surprise, surprise, there were some for me.
It helped me make some money on my first day, and that was pretty cool. I wouldn’t have done that if I hadn’t been prompted by this thing to do it. And there’s a check on its way to me in the mail. Fantastic. [But] that ends pretty quickly, right? That was a one-time shot, but it’s not a thing that’s repeatable. That’s more like a party trick-type thing.
Kirsten: I mean, you just killed your own argument. I don’t see how that wouldn’t become wildly popular.
Sean: The more sustainable version of that, and the thing that Meta’s talked up a lot over the last couple of days, is taking that idea and applying it to your real, true everyday financials, like giving it your information for your credit card, your Gmail account, all this other stuff, do things that we’ve seen other companies do, like Rocket Money or whatever, where it’ll go cancel subscriptions that you’re not using or identify double charges, things that frankly the credit card company should be doing already.
And at that point you just run into that trust wall with Meta. I think one of the reasons that I was willing to explore this and was curious to stick with it a little bit — even through to today — is that somewhat shockingly, when I downloaded it, I just assumed that it would like really instantly prompt me and like plug me right into Threads, Instagram, Facebook, which I don’t really use ever, and pull up that context immediately.
But it didn’t. And it was working with me like I was a stranger at first, which made me more willing to use it, because I didn’t feel like Meta had everything on me already. But you can see, as you start to use it, it really tries to grab you and pull those things into the system, so that it can learn all this stuff about you.
I don’t know that I will ever trust Meta the same way. I think it’s an interesting timing for me, having just upgraded my iPhone and getting onto the new iOS with the new Siri that actually works and can do some controls on your phone in a way that is surprising and helpful, that it’s never been able to do. [I’ve been] thinking about how much I’ve been using that over the last week and how much more how much more willing I would be to have the Siri version of Muse take that information, because I just trust Apple more with that really sensitive information and not only trust it with the information from a cybersecurity perspective, but from the fact that its business is not to sell me a bunch of crappy ads.
Meta’s business is to sell you ads. And yes, they’ll make the argument that the more they know about you, the more accurate and interesting the ads will be — wake me up when we get to that fever dream.
And beyond the one-time money lever that I got, which was great, I don’t feel like I’ve found anything else that’s really all that useful — other than the fact that it is, to Anthony’s point, really tailored at keeping it sort of consumer-y in your interactions with it, with which I do think helps it and is why people are talking about it so much.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
>
The House | Unison’s Andrea Egan: I’m Not Rejoining Labour Until The Party Inspires Me
All the goals from the Women's Super League – matchweek four
You are the new Scotland boss – what would you do?
-
movies4 months agoSearch For Canadian TV Actor Stewart McLean Now Homicide Investigation
-
Fashion9 years agoThese ’90s fashion trends are making a comeback in 2017
-
Fashion9 years ago
According to Dior Couture, this taboo fashion accessory is back
-
Fashion9 years ago
Model Jocelyn Chew’s Instagram is the best vacation you’ve ever had
-
Fashion9 years ago
Emily Ratajkowski channels back-to-school style
-
Fashion9 years ago
9 Celebrities who have spoken out about being photoshopped
-
Anime4 months agoRurouni Kenshin: Hokkaido Arc Manga Takes 1-Issue Break – News
-
Anime3 months agoHIDIVE to Stream English Dubs for The World Is Dancing, The Forsaken Saintess and Her Foodie Roadtrip in Another World, The Dangers in My Heart: The Movie Anime – News