New chip design brings energy-efficient AI computing closer to phones and laptops

In project CONVOLVE, researchers successfully used new chip-design techniques to develop powerful AI computing chips close to where data is generated and used, often referred to as edge computing. September 8, 2026 New chip design brings energy-efficient AI computing closer to phones and laptops by Nicole van Overveld, Eindhoven University of Technology edited by Sadie Harley, reviewed by Andrew Zinin Sadie Harley Scientific Editor Meet our editorial team Behind our editorial process Andrew Zinin Chief Editor Meet our editorial team Behind our editorial process Editors' notes This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked trusted source proofread The GIST Add as preferred source The new Chimera chip, developed as part of the Convolve project.

The Chimera is a mythical creature composed of parts of a lion, a goat, and a snake. It thus symbolizes the chip, in which various AI accelerators—each with their own characteristics and strengths—work together. The aim of the researchers working on CONVOLVE was to find a new way to develop clever yet powerful chips to execute AI calculations close to the user, in their own laptops and smartphones.

TU/e researchers and their partners on the CONVOLVE team presented their successes at this year's ESSERC Conference in Spain. "We were looking to design and build an efficient chip for so-called edge computing," lead researcher Manil Dev Gomony explains. "This means that a user who asks AI a question can run the model locally, on their device at the 'edge' of the cloud, instead of sending this command to a remote data center somewhere on Earth and receiving the answer." "We need fast and efficient computing power for these hubs in our homes as well," said Gomony, a professor of low-power digital hardware design.

"Additionally, we are getting an increasing number of smart devices in our homes (solar panels, electric vehicles, heat pumps, in-home energy storage, etc.) that need to quickly determine the best way to operate, given grid congestion, energy prices and many other variables." That is why Gomony and his colleagues developed and manufactured a powerful chip to perform selected AI calculations close to the user: in their own device and with lower energy demands. Edge computing versus the cloud In 2022, when the researchers at TU/e started their project, the use of AI was already quite common. The exponential growth of GenAI use in 2026 is staggering and is placing ever-greater demands on high-powered chips used in large data centers.

This drives up energy consumption and the construction of enormous data centers, which use huge amounts of water and have detrimental effects on the environment. These are exactly the issues Gomony and his colleagues hope to remedy with their revolutionary approach to chip design and computing power.

"Calculations closer to the user cost less energy than sending those requests to the supercomputing data centers that are being used nowadays. And the chips used in data centers are large and get really hot, so they require their own integrated fans and cooling water, putting a strain on local communities.

This makes them not only energy-consuming but also far too large to fit into a laptop, home automation device or smartphone." Different design approach One of the things the TU/e-led CONVOLVE team changed was the chip design methodology. They developed their methodology specifically to make energy-efficient chips.

With research partners in Leuven, Delft, Zurich and many industry partners, each bringing unique expertise to the project, they managed to design and build the chip they all envisioned. Their measurements prove that they succeeded.

Gomony said, "Within CONVOLVE, we used a cross-layer design approach. That means that instead of developing the AI algorithm, processor architecture, memory system and circuits independently, the team optimized these layers together." "This allows us to balance several important aspects of the chip's properties (such as accuracy, programmability, processing speed, silicon area, and energy consumption) right from the start of the design process." Combination "We combined programmable RISC-V processors with specialized AI accelerators, including memory-centric and neuromorphic computing techniques, to help us achieve our goals.

That was important because we aimed to reduce the movement of data between memory and processing units. We did that to conserve energy by design.

Data movement is often a major source of energy consumption in AI hardware, including our chip." Gomony said, "To prove we had succeeded in achieving those goals, we evaluated prototype chips we had fabricated, rather than simulations alone. Our team applied realistic AI workloads to the chip, so we could see how well it performed.

Researchers measured energy efficiency, throughput, latency, silicon area and application accuracy and compared the results with relevant existing designs. The results proved our point." A European answer There is an enormous increase in computing power.

One of the measures used to describe computing speed is the petaflop; one petaflop is 1,000,000,000,000,000 mathematical calculations every second. In 2010, this would have taken 625,000 iPhone 4s.

"Just look at how computing power has evolved.