Tech
Cracking a long-standing weakness in a classic algorithm for programming reconfigurable chips
Researchers from EPFL, AMD, and the University of Novi Sad have uncovered a long-standing inefficiency in the algorithm that programs millions of reconfigurable chips used worldwide, a discovery that could reshape how future generations of these are designed and programmed.
Many industries, including telecoms, automotive, aerospace and particle physics rely on a special breed of chip called the Field-Programmable Gate Array (FPGA). Unlike traditional chips, FPGAs can be reconfigured almost endlessly, making them invaluable in fast-moving fields where designing a custom chip would take years and cost a fortune. But this flexibility comes with a catch: FPGA efficiency depends heavily on the software used to program them.
Since the late 1990s, an algorithm known as PathFinder has been the backbone of FPGA routing. Its job: connecting thousands of tiny circuit components without creating overlaps.
For decades, it worked so well that it became the standard. However, as circuits grew larger, engineers began encountering frustrating slowdowns and occasional outright failures. Designs that should have worked were often labeled “unroutable.”
Now, with colleagues from the University of Novi Sad and the technology company AMD, researchers from the Parallel Systems Architecture Laboratory (PARSA) in the School of Computer and Communication Sciences have come one step closer to untangling the inner workings of this classic algorithm.
In their paper, which received the Best Paper Award at the 33rd IEEE International Symposium on Field-Programmable Custom Computing Machines, they revealed why these failures happen and how PathFinder’s limits can be overcome.
Cracks in the algorithm
“In fact, it’s not surprising that PathFinder sometimes fails,” explained Shashwat Shrivastava, Ph.D. student with PARSA and first author of the paper.
“Very early on, researchers showed that the problem behind FPGA routing is extremely hard. Later, the creators of the original algorithm, together with a few collaborators, found cases where PathFinder would never succeed—but they noted such cases wouldn’t appear in practice.”
For decades, it seemed they were correct—PathFinder worked surprisingly well.
“PathFinder worked so well, in fact, that when it failed, people rarely questioned the algorithm. Instead of venturing inside to see what was going on, they tweaked its parameters, modified circuits, or switched to larger FPGAs,” added Stefan Nikolić, an EPFL alumnus and now a professor at the University of Novi Sad.
“Part of the reason for this is that it is rather difficult to understand what PathFinder is actually doing on examples of practical importance. Modern circuits are so large that their signals form veritable on-chip jungles.”
Enter the forest
“So, we really needed to look at the individual trees in that jungle,” continued Shrivastava, “and I really mean trees. Each signal—a connection that carries information between circuit components—must reach multiple destinations without overlapping other signals. FPGA routing is essentially about building one tree for each signal on the chip.”
While working on another project that relied on PathFinder, the team kept seeing results that defied intuition. At first, they blamed external factors, not the algorithm itself. Eventually, they realized they needed controlled examples: small, tricky cases where a solution definitely existed, and in which PathFinder should succeed.
“We needed real, practical examples, and lots of them, to understand what was really going on,” Shrivastava explains. “So, we built a framework to automatically extract small, hard problems from real circuits. Watching how PathFinder struggled with these helped us uncover issues that had remained hidden for a very long time.”
Power in partnership
“This breakthrough would have been much harder without industry support,” said Mirjana Stojilović, Shrivastava’s Ph.D. advisor. “From the start, we collaborated with Chirag Ravishankar and Dinesh Gaitonde from AMD. They helped us model FPGAs as close as possible to commercial devices, ensuring our findings had real-world impact.”
Once the framework was ready, things moved quickly. The team found that PathFinder often built routing trees larger than necessary, increasing the risk of overlaps. The problem came from the order in which it created and added new branches to the trees.
“In retrospect, this is intuitive, but somehow it went largely unnoticed for many years,” Shrivastava said. “Our first solution was simple: try different orders and pick the one that results in the smallest tree. Experimentally, it worked surprisingly well.”
The team is now exploring more scalable solutions. “I am especially proud that Summer@EPFL interns have been contributing significantly. One of them, Sun Tanaka, is also a co-author of the paper,” added Stojilović.
“Our discovery could reshape how millions of FPGAs are programmed and influence the design of future generations of these reconfigurable chips.”
More information:
Shashwat Shrivastava et al, Guaranteed Yet Hard to Find: Uncovering FPGA Routing Convergence Paradox, 2025 IEEE 33rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (2025). DOI: 10.1109/fccm62733.2025.00060
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Cracking a long-standing weakness in a classic algorithm for programming reconfigurable chips (2025, October 3)
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Tech
Google’s Pixel 10a May Not Be Exciting, but It’s Still an Unbeatable Value
The screen is brighter now, reaching a peak brightness of 3,000 nits, and I haven’t had any trouble reading it in sunny conditions (though it hasn’t been as sunny as I’d like it to be these past few weeks). I appreciate the glass upgrade from Gorilla Glass 3 to Gorilla Glass 7i. It should be more protective, and anecdotally, I don’t see a single scratch on the Pixel 10a’s screen after two weeks of use. (I’d still snag a screen protector to be safe.)
Photograph: Julian Chokkattu
Another notable upgrade is in charging speeds—30-watt wired charging and 10-watt wireless charging. I’ll admit I haven’t noticed the benefits of this yet, since I’m often recharging the phone overnight. You can get up to 50 percent in 30 minutes of charging with a compatible adapter, and that has lined up with my testing.
My biggest gripe? Google should have taken this opportunity to add its Pixelsnap wireless charging magnets to the back of this phone. That would help align the Pixel 10a even more with the Pixel 10 series and bring Qi2 wireless charging into a more affordable realm—actually raising the bar, which wouldn’t be a first for the A-series. After all, Apple did exactly that with the new iPhone 17e, adding MagSafe to the table. Or heck, at least make the Pixel 10a Qi2 Ready like Samsung’s smartphones, so people who use a magnetic case can take advantage of faster wireless charging speeds.
Battery life has been OK. With average use, the Pixel 10a comfortably lasts me a full day, but it still requires daily charging. With heavier use, like when I’m traveling, I’ve had to charge the phone in the afternoon a few times to make sure it didn’t die before I got into bed. This is a fairly big battery for its size, but I think there’s more Google could do to extend juice, akin to Motorola’s Moto G Power 2026.
Tech
The Colorful MacBook Neo Is Apple’s Cheapest Laptop Ever
After a week of product announcements—starting with the iPhone 17e, a refreshed iPad Air, and more powerful MacBook Pro models—Apple has unveiled a new category in its laptop lineup for the first time in a while: the “MacBook Neo.”
Photograph: Julian Chokkattu
Positioned below the MacBook Air as an entry-level machine, this new MacBook is the most affordable laptop the company has ever made, with a starting price of $599. While it’s been possible to buy a new MacBook Air at lower prices—like the 2020 M1 MacBook Air Apple sold for several years for $699 exclusively through Walmart—this is officially the cheapest MacBook out the gate.
Aside from the price, its approach to color also makes it unique among the other MacBooks in Apple’s lineup. You have several color options, including Silver, Indigo, Blush, and Citrus. The colors harken back a bit to the iBook G3 of yesteryear and are akin to the current iMac design. In person, the colors aren’t a bright and bold as expected, still exhibiting a more subtle hue. Apple says the aluminum device weighs 2.7 pounds, which is the same as the 13-inch MacBook Air. We’re still waiting on official measurements on the thickness.
Despite its price, Apple doesn’t appear to be cutting corners on the quality of the screen. With a resolution of 2408 by 1506 and up to 500 nits of brightness, Apple boasts that it is “both brighter and higher in resolution than most PC laptops in this price range.” The display doesn’t use a notch for the webcam like the MacBook Air or MacBook Pro. There’s a 1080p camera, a Touch ID sensor, and side-firing speakers with Dolby Atmos. Unfortunately, the Touch ID sensor is only available on the $699 model, which comes with 512 GB of storage.
The MacBook Neo does make plenty of other concessions to hit its aggressive price though. It’s powered by the A18 Pro chip—the same processor inside the iPhone 16 Pro and 16 Pro Max. Yup—you read that right. iPads have used Mac chips for years, but now a MacBook is using an iPhone chip. Still, this processor should deliver more power than the original M1 chip in the MacBook Air. Apple claims the chip gives the MacBook Neo up to 16 hours of battery life. That’s less than the MacBook Air or MacBook Pro. Apple also says the chip is up to 50 percent faster in daily tasks like web browsing than “the bestselling PC with the latest chipping Intel Core Ultra 5.” According to the liner notes, this was based on a Speedometer test, a popular browser-based benchmark.
Other compromises to the device are the use of a mechanical multi-touch trackpad (rather than one that uses haptic feedback), a non-backlit keyboard, and the more limited port selection. The use of the iPhone chip means this MacBook only supports one external monitor through one if its two USB-C ports. Either port can be used for charging. There’s also a headphone jack, located in an odd position next to the side-firing speakers near the front of the device. While technically this is the same amount of USB-C ports as the MacBook Air, it’s missing the magnetic MagSafe 3 charging port, which frees up one of the USB-C ports.
Tech
What It’s Like to Have a Brain Implant for 5 Years
Initially, Gorham used his brain-computer interface for single clicks, Oxley says. Then he moved on to multi-clicks and eventually sliding control, which is akin to turning up a volume knob. Now he can move a computer cursor, an example of 2D control—horizontal and vertical movements within a two-dimensional plane.
Over the years, Gorham has gotten to try out different devices using his implant. Zafar Faraz, a field clinical engineer for Synchron, says Gorham directly contributed to the development of Switch Control, a new accessibility feature Apple announced last year that allows brain-computer interface users the ability to control iPhones, iPads, and the Vision Pro with their thoughts.
In a video demonstration shown at an Nvidia conference last year in San Jose, California, Gorham demonstrates using his implant to play music from a smart speaker, turn on a fan, adjust his lights, activate an automatic pet feeder, and run a robotic vacuum in his home in Melbourne, Australia.
“Rodney has been pushing the boundaries of what is possible,” Faraz says.
As a field clinical engineer, Faraz visits Gorham in his home twice a week to lead sessions on his brain-computer interface. It’s Faraz’s job to monitor the performance of the device, troubleshoot problems, and also learn the range of things that Gorham can and can’t do with it. Synchron relies on this data to improve the reliability and user-friendliness of its system.
In the years he’s been working with Gorham, the two have done a lot of experimenting to see what’s possible with the implant. Once, Faraz says, he had Gorham using two iPads side by side, switching between playing a game on one and listening to music on the other. Another time, Gorham played a computer game in which he had to grab blocks on a shelf. The game was tied to an actual robotic arm at the University of Melbourne, about six miles from Gorham’s home, that remotely moved real blocks in a lab.
Gorham, who was an IBM software salesman before he was diagnosed with ALS in 2016, has relished being such a key part of the development of the technology, his wife Caroline says.
“It fits Rodney’s set of life skills,” she says. “He spent 30 years in IT, talking to customers, finding out what they needed from their software, and then going back to the techos to actually develop what the customer needed. Now it’s sort of flipped around the other way.” After a session with Faraz, Gorham will often be smiling ear to ear.
Through field visits, the Synchron team realized it needed to change the setup of its system. Currently, a wire cable with a paddle on one end needs to sit on top of the user’s chest. The paddle collects the brain signals that are beamed through the chest and transmits them via the wire to an external unit that translates those signals into commands. In its second generation system, Synchron is removing that wire.
“If you have a wearable component where there’s a delicate communication layer, we learned that that’s a problem,” Oxley says. “With a paralyzed population, you have to depend on someone to come and modify the wearable components and make sure the link is working. That was a huge learning piece for us.”
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