Researchers on the Korea Superior Institute of Science and Expertise (KAIST) have developed energy-efficient NPU expertise that demonstrates substantial efficiency enhancements in laboratory testing.
Their specialised AI chip ran AI fashions 60% sooner whereas utilizing 44% much less electrical energy than the graphics playing cards at present powering most AI methods, based mostly on outcomes from managed experiments.
To place it merely, the analysis, led by Professor Jongse Park from KAIST’s Faculty of Computing in collaboration with HyperAccel Inc., addresses one of the urgent challenges in fashionable AI infrastructure: the big vitality and {hardware} necessities of large-scale generative AI fashions.
Present methods corresponding to OpenAI’s ChatGPT-4 and Google’s Gemini 2.5 demand not solely excessive reminiscence bandwidth but additionally substantial reminiscence capability, driving firms like Microsoft and Google to buy tons of of hundreds of NVIDIA GPUs.
The reminiscence bottleneck problem
The core innovation lies within the group’s method to fixing reminiscence bottleneck points that plague present AI infrastructure. Their energy-efficient NPU expertise focuses on “light-weight” the inference course of whereas minimising accuracy loss—a essential steadiness that has confirmed difficult for earlier options.
PhD scholar Minsu Kim and Dr Seongmin Hong from HyperAccel Inc., serving as co-first authors, introduced their findings on the 2025 Worldwide Symposium on Pc Structure (ISCA 2025) in Tokyo. The analysis paper, titled “Oaken: Quick and Environment friendly LLM Serving with On-line-Offline Hybrid KV Cache Quantization,” particulars their complete method to the issue.
The expertise centres on KV cache quantisation, which the researchers establish as accounting for most reminiscence utilization in generative AI methods. By optimising this element, the group permits the identical degree of AI infrastructure efficiency utilizing fewer NPU gadgets in comparison with conventional GPU-based methods.
Technical innovation and structure
The KAIST group’s energy-efficient NPU expertise employs a three-pronged quantisation algorithm: threshold-based online-offline hybrid quantisation, group-shift quantisation, and fused dense-and-sparse encoding. This method permits the system to combine with present reminiscence interfaces with out requiring adjustments to operational logic in present NPU architectures.
The {hardware} structure incorporates page-level reminiscence administration methods for environment friendly utilisation of restricted reminiscence bandwidth and capability. Moreover, the group launched new encoding methods particularly optimised for quantised KV cache, addressing the distinctive necessities of their method.
“This analysis, by joint work with HyperAccel Inc., discovered an answer in generative AI inference light-weighting algorithms and succeeded in creating a core NPU expertise that may remedy the reminiscence downside,” Professor Park defined.
“By way of this expertise, we carried out an NPU with over 60% improved efficiency in comparison with the newest GPUs by combining quantisation methods that cut back reminiscence necessities whereas sustaining inference accuracy.”
Sustainability implications
The environmental impression of AI infrastructure has develop into a rising concern as generative AI adoption accelerates. The energy-efficient NPU expertise developed by KAIST provides a possible path towards extra sustainable AI operations.
With 44% decrease energy consumption in comparison with present GPU options, widespread adoption may considerably cut back the carbon footprint of AI cloud providers. Nevertheless, the expertise’s real-world impression will depend upon a number of components, together with manufacturing scalability, cost-effectiveness, and trade adoption charges.
The researchers acknowledge that their answer represents a big step ahead, however widespread implementation would require continued improvement and trade collaboration.
Business context and future outlook
The timing of this energy-efficient NPU expertise breakthrough is especially related as AI firms face growing strain to steadiness efficiency with sustainability. The present GPU-dominated market has created provide chain constraints and elevated prices, making various options more and more enticing.
Professor Park famous that the expertise “has demonstrated the opportunity of implementing high-performance, low-power infrastructure specialised for generative AI, and is anticipated to play a key function not solely in AI cloud information centres but additionally within the AI transformation (AX) setting represented by dynamic, executable AI corresponding to agentic AI.”
The analysis represents a big step towards extra sustainable AI infrastructure, however its final impression can be decided by how successfully it may be scaled and deployed in business environments. Because the AI trade continues to grapple with vitality consumption issues, improvements like KAIST’s energy-efficient NPU expertise provide hope for a extra sustainable future in synthetic intelligence computing.
(Photograph by Korea Superior Institute of Science and Expertise)
See additionally: The 6 practices that guarantee extra sustainable information centre operations
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