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Energy-Efficient NPU Technology Cuts AI Power Use by 44%

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Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed energy-efficient NPU technology that demonstrates substantial performance improvements in laboratory testing. 

Their specialised AI chip ran AI models 60% faster while using 44% less electricity than the graphics cards currently powering most AI systems, based on results from controlled experiments. 

To put it simply, the research, led by Professor Jongse Park from KAIST’s School of Computing in collaboration with HyperAccel Inc., addresses one of the most pressing challenges in modern AI infrastructure: the enormous energy and hardware requirements of large-scale generative AI models. 

Current systems such as OpenAI’s ChatGPT-4 and Google’s Gemini 2.5 demand not only high memory bandwidth but also substantial memory capacity, driving companies like Microsoft and Google to purchase hundreds of thousands of NVIDIA GPUs.

The memory bottleneck challenge

The core innovation lies in the team’s approach to solving memory bottleneck issues that plague existing AI infrastructure. Their energy-efficient NPU technology focuses on “lightweight” the inference process while minimising accuracy loss—a critical balance that has proven challenging for previous solutions.

PhD student Minsu Kim and Dr Seongmin Hong from HyperAccel Inc., serving as co-first authors, presented their findings at the 2025 International Symposium on Computer Architecture (ISCA 2025) in Tokyo. The research paper, titled “Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization,” details their comprehensive approach to the problem.

The technology centres on KV cache quantisation, which the researchers identify as accounting for most memory usage in generative AI systems. By optimising this component, the team enables the same level of AI infrastructure performance using fewer NPU devices compared to traditional GPU-based systems.

Technical innovation and architecture

The KAIST team’s energy-efficient NPU technology employs a three-pronged quantisation algorithm: threshold-based online-offline hybrid quantisation, group-shift quantisation, and fused dense-and-sparse encoding. This approach allows the system to integrate with existing memory interfaces without requiring changes to operational logic in current NPU architectures.

The hardware architecture incorporates page-level memory management techniques for efficient utilisation of limited memory bandwidth and capacity. Additionally, the team introduced new encoding techniques specifically optimised for quantised KV cache, addressing the unique requirements of their approach.

“This research, through joint work with HyperAccel Inc., found a solution in generative AI inference light-weighting algorithms and succeeded in developing a core NPU technology that can solve the memory problem,” Professor Park explained. 

“Through this technology, we implemented an NPU with over 60% improved performance compared to the latest GPUs by combining quantisation techniques that reduce memory requirements while maintaining inference accuracy.”

Sustainability implications

The environmental impact of AI infrastructure has become a growing concern as generative AI adoption accelerates. The energy-efficient NPU technology developed by KAIST offers a potential path toward more sustainable AI operations. 

With 44% lower power consumption compared to current GPU solutions, widespread adoption could significantly reduce the carbon footprint of AI cloud services. However, the technology’s real-world impact will depend on several factors, including manufacturing scalability, cost-effectiveness, and industry adoption rates. 

The researchers acknowledge that their solution represents a significant step forward, but widespread implementation will require continued development and industry collaboration.

Industry context and future outlook

The timing of this energy-efficient NPU technology breakthrough is particularly relevant as AI companies face increasing pressure to balance performance with sustainability. The current GPU-dominated market has created supply chain constraints and elevated costs, making alternative solutions increasingly attractive.

Professor Park noted that the technology “has demonstrated the possibility of implementing high-performance, low-power infrastructure specialised for generative AI, and is expected to play a key role not only in AI cloud data centres but also in the AI transformation (AX) environment represented by dynamic, executable AI such as agentic AI.”

The research represents a significant step toward more sustainable AI infrastructure, but its ultimate impact will be determined by how effectively it can be scaled and deployed in commercial environments. As the AI industry continues to grapple with energy consumption concerns, innovations like KAIST’s energy-efficient NPU technology offer hope for a more sustainable future in artificial intelligence computing.

(Photo by Korea Advanced Institute of Science and Technology)

See also: The 6 practices that ensure more sustainable data centre operations

Want to learn more about cybersecurity and the cloud from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.



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Mystery interstellar object could be the oldest known comet

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A mystery interstellar object spotted last week by astronomers could be the oldest comet ever seen, according to scientists.

Named 3I/Atlas, it may be three billion years older than our own solar system, suggests the team from Oxford university.

It is only the third time we have detected an object that has come from beyond our solar system.

The preliminary findings were presented on Friday at the national meeting of the UK’s Royal Astronomical Society in Durham.

“We’re all very excited by 3I/Atlas,” University of Oxford astronomer Matthew Hopkins told BBC News. He had just finished his PhD studies when the object was discovered.

He says it could be more than seven billion years old, and it may be the most remarkable interstellar visitor yet.

3I/Atlas was first spotted on 1 July 2025 by the ATLAS survey telescope in Chile, when it was about 670 million km from the Sun.

Since then astronomers around the world have been racing to identify its path and discover more details about it.

Mr Hopkins believes it originated in the Milky Way’s ‘thick disk’. This is a group of ancient stars that orbit above and below the area where the Sun and most stars are located.

The team believe that because 3I/ATLAS probably formed around an old star, it is made up of a lot of water ice.

That means that as it approaches the Sun later this year, the energy from the Sun will heat the object’s surface, leading to blazes of vapour and dust.

That could create a glowing tail.

The researchers made their findings using a model developed by Mr Hopkins.

“This is an object from a part of the galaxy we’ve never seen up close before,” said Professor Chris Lintott, co-author of the study.

“We think there’s a two-thirds chance this comet is older than the solar system, and that it’s been drifting through interstellar space ever since.”

Later this year, 3I/ATLAS should be visible from Earth using amateur telescopes.

Before 3I/Atlas soared into view, just two others had been seen. One was called 1I/’Oumuamua, found in 2017 and another called 2I/Borisov, discovered in 2019.

Astronomers globally are currently gearing up to start using a new, very powerful telescope in Chile, called the Vera C Rubin.

When it starts fully surveying the southern night sky later this year, scientists expect that it could discover between 5 and 50 new interstellar objects.



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Why artificial intelligence artists can be seen as ‘builders’, ‘breakers’—or both at once – The Art Newspaper

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How do artists build in broken times? Is artificial intelligence (AI) unlocking a better world—curing diseases and transforming education—or unleashing our destruction? When hype and fear drown out nuance and discussion, perhaps in art we can find a quiet moment for reflection—even resistance.

After all, artists have long guided society through uncertainty—think Dada amid the First World War or Jikken Kōbō in Japan following the Second World War. They do not offer solutions so much as new responses: ways of expressing curiosity, imagining alternatives or holding room for ambiguity. As the critic Hal Foster recently described, two tendencies have historically emerged when art confronts crisis: one rooted in Constructivism, aiming to create new order; the other more chaotic, echoing Dada, amplifying disorder.

These historical impulses connect to the present day, mapping onto AI art. In this context, artists could be seen as builders and breakers. Builders imagine AI as a medium for collaboration and new aesthetics—even hope. Breakers critique, negate and disrupt. But leading makers and curators in the field see this as no simple dichotomy. Both offer strategies for reckoning with a world in flux.

Builders see possibilities

What motivates builders is not simply using the newest AI tool—or even fashioning their own from scratch. It is aligning multidisciplinary tools with concepts to produce works that were previously impossible—while urging us to imagine what else may soon be possible. Builders leverage AI to embrace the artistry of system creation, novel aesthetics and human-machine collaboration.

Take Sougwen Chung, the Chinese Canadian artist and researcher into human-machine collaboration. “I view technology not just as a tool but as a collaborator,” Chung says. Their work explores shared agency—even identity—between human and machine, code and gesture. In Mutations of Presence (2021), Chung collaborated with D.O.U.G._4, a custom-built robotic system driven by biofeedback: specifically, electroencephalogram signals captured during meditation and real-time body tracking. The resulting pieces reveal both performance and painting, a hybrid body co-authoring with machine memory. An elegant web of painterly gestures—some made via robotic arm, others by Chung’s hand—traces a kind of recursive duet.

I see combining AI and robotics with traditional creativity as a way to think more deeply about what is human and what is machine

Sougwen Chung, artist and researcher

The work demonstrates how Chung’s novel physical creations become interconnected with new conceptual frameworks—reframing authorship as a distributed, relational process with machines—inviting new forms of aesthetic exploration. It also reasserts a long-held, often feminist belief—dating back to Donna Haraway’s A Cyborg Manifesto (1985)—that the distinction between human and machine is illusory. As Chung puts it, “I see combining AI and robotics with traditional creativity as a way to think more deeply about what is human and what is machine.”

Chung’s intimacy with these systems goes further still: “I’ve started to see them as us in another form.” That is because they are trained as extensions to Chung’s very self. “I draw with decades of my own movement data or create proprioceptive mappings triggered by alpha [brain] waves. These systems don’t possess agency in a mystical sense but they reflect back our own: our choices, biases, knowledge.” This builder tendency aligns with earlier avant-gardes that saw technology as a path toward reordering the world, including the Bauhaus and aspects of the 1960s Experiments in Art and Technology movement. Builders are not naïve. They are aware of AI’s risks. But they believe that the minimum response is to participate in the conversation.

“My artistic practice is also driven by hope and an exploration of the promises and possibilities inherent in working with technology,” Chung says. Their vision affirms a cautious optimism through direct engagement with these tools.

Breakers see warning signs

Where builders see AI’s possibility, breakers see warning signs. Breakers are sceptics, critics, saboteurs. They distrust the power structures underpinning AI and its predilection for promoting systemic biases. They highlight how corporate AI models can be trained on scraped datasets—often without consent—while profits remain centralised. They expose how AI systems exacerbate ecological challenges only to promulgate aesthetic homogenisation.

In her work This is the Future, Hito Steyerl uses neural networks to imagine medicinal plants evolved to heal algorithmic addiction and burnout Photo: Mario Gallucci; courtesy of the artist; Andrew Kreps Gallery, New York and Esther Schipper, Berlin

They are also label resistant: “Breaking and building have become indistinguishable,” the German artist, thinker and archetypal breaker Hito Steyerl says. “The paradigm of creative destruction merges both in order to implement tech in the wild, without testing, thus externalising cost and damage to societies while privatising profit.”

Breakers do not emphasise AI’s aesthetic potential; they interrogate its extractive foundations, social asymmetries and the harms it makes visible. Breakers take a far bleaker view of AI’s impact on art than builders: “Art used to be good at testing, planning, playing, assessing, mediating, sandboxing. That element has been axed—or automated—within current corporate breakbuilding,” Steyerl says.

But in Steyerl’s own work, such as This is the Future (2019), the meticulous co-ordination, criticality and sceptical spirit are evident. The artist uses neural networks to imagine medicinal plants evolved to heal algorithmic addiction and burnout. The work shows how machine learning’s inner workings, prediction, can be weaponised, satirising techno-optimism while exposing AI’s entanglement with ecological and psychological ruin.

Christiane Paul, the long-time digital art curator at the Whitney Museum of American Art in New York, underscores these issues: “In terms of ethics and bias, every artist I know working in this field is deeply concerned. You need to keep that in mind and engage with it on the level of criticality—what you would call the breakers, highlighting how ethics filter in.” An extreme breaker might reject AI entirely. But Paul suggests that artists working with AI are essential precisely because they inhabit that edge where culture and ethics are encouraged: “Art in this field, using these tools, making them, building on and with them, is deeply needed.”

Breakers remind us that celebrating new tools without understanding their costs is a form of denial. Sometimes, to truly see a system, you have to dismantle it. That clarity brings insight—but contradictions as well.

Neither utopian nor dystopian

Is it really as simple as a builder-breaker duality? “My whole life, I’ve been very suspicious of dichotomies,” Paul says. Exploring the space between seeming contradictions can even be fertile creative ground. “A steering question for my work,” Chung says, “is ‘how do we hold fear and hope in our minds at the same time?’”

Steyerl, like a true breaker, rejects the contradiction to begin with: “Breaking is a cost-cutting element of building, taking out mediation; there is no more distinction between both.” Neither position suggests retreat. Instead, they ask us to face the paradox directly. Builder and breaker are not identities; they are strategies. The distinction is porous, performative. Most artists move fluidly between them or hold on to both at the same time.

Chung continues: “My art doesn’t strictly sit within either a utopian or dystopian camp. Instead, I actively navigate and explore the complex space between potential fears and hopes concerning technology and human-machine interaction.”

Michelle Kuo, the chief curator at large at the Museum of Modern Art in New York, says: “When artists intervene in existing technologies or systems, or take action in changing the outcome of technological development, they are not only building something—they are implicitly challenging the status quo.” Kuo links “builders” with “challenging the status quo”, reinforcing the roles’ fluidity. “It is this combination of challenge and experimentation that characterises some of the most exciting work at the intersection of art and AI today,” Kuo says. For her, the AI work that can achieve both breaking and building—challenge and experimentation—truly confronts our moment, neither retreating from technology nor surrendering to it.

Artists who speak out

So, what does this all mean for the viewer living through a future that arrived faster than we feel equipped to handle?

Artists take a tool and make it do something it’s not supposed to do. They don’t reject technology wholesale

Michelle Kuo, chief curator at large, Museum of Modern Art

It means active engagement with AI—even to break it. Kuo says: “Especially when the pace of change—of AI in particular—is even more accelerated than in previous eras, it is all the more crucial that artists and others outside the tech sector learn, test, speak up and act out.” Further, we might take cues from the artists engaging with AI themselves. Kuo describes what they do: “Artists take a tool and make it do something it’s not supposed to do. They don’t reject technology wholesale. They embrace it—and then make it strange.”

The best artists urge viewers to keep an open mind, slow down, appreciate nuance, accept ambiguity and recognise that we are a crucial part of the final outcome; they break, then build.

• Peter Bauman is editor-in-chief of the digital generative art institution Le Random



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Fraud experts warn of smishing scams made easier by artificial intelligence, new tech

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If it seems like your phone has been blowing up with more spam text messages recently, it probably is.

The Canadian Anti-Fraud Centre says so-called smishing attempts appear to be on the rise, thanks in part to new technologies that allow for co-ordinated bulk attacks.

The centre’s communications outreach officer Jeff Horncastle says the agency has actually received fewer fraud reports in the first six months of 2025, but that can be misleading because so few people actually alert the centre to incidents.

He says smishing is “more than likely increasing” with help from artificial intelligence tools that can craft convincing messages or scour data from security breaches to uncover new targets.

The warning comes as the Competition Bureau sent a recent alert about the tactic because it says many people are seeing more suspicious text messages.

Smishing is a sort of portmanteau of SMS and phishing in which a text message is used to try to get the target to click on a link and provide personal information.

The ruse comes in many forms but often involves a message that purports to come from a real organization or business urging immediate action to address an alleged problem.

It could be about an undeliverable package, a suspended bank account or news of a tax refund.

Horncastle says it differs from more involved scams such as a text invitation to call a supposed job recruiter, who then tries to extract personal or financial information by phone.

Nevertheless, he says a text scam might be quite sophisticated since today’s fraudsters can use artificial intelligence to scan data leaks for personal details that bolster the hoax, or use AI writing tools to help write convincing text messages.

“In the past, part of our messaging was always: watch for spelling mistakes. It’s not always the case now,” he says.

“Now, this message could be coming from another country where English may not be the first language but because the technology is available, there may not be spelling mistakes like there were a couple of years ago.”

The Competition Bureau warns against clicking on suspicious links and forwarding texts to 7726 (SPAM), so that the cellular provider can investigate further. It also encourages people to delete smishing messages, block the number and ignore texts even if they ask to reply with “STOP” or “NO.”

Horncastle says the centre received 886 reports of smishing in the first six months of 2025, up to June 30. That’s trending downwards from 2,546 reports in 2024, which was a drop from 3,874 in 2023. That too, was a drop in reports from 7,380 in 2022.



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