Articles tagged with "energy-efficient-computing"
CES 2026: Everything revealed, from Nvidia’s debuts to AMD’s new chips to Razer’s AI oddities
CES 2026 in Las Vegas showcased major advancements with a strong emphasis on AI across various industries. Nvidia unveiled its Rubin computing architecture, designed to replace the Blackwell architecture later this year, offering enhanced speed and storage to meet growing AI computational demands. Nvidia also highlighted AI models for autonomous vehicles, reflecting its broader strategy to integrate AI into physical-world applications. Meanwhile, AMD’s CEO Lisa Su presented new Ryzen AI 400 Series processors and emphasized partnerships with AI leaders like OpenAI and Luma AI, underscoring AMD’s commitment to AI-driven innovation. Other notable reveals included Hyundai and Boston Dynamics partnering with Google’s AI research lab to enhance and operate Atlas robots, signaling significant collaboration in robotics. Amazon introduced Alexa+ with expanded chatbot capabilities accessible via browsers and apps, alongside updates to its Ring security products featuring fire alerts and third-party app integrations. Razer deviated from its usual hardware focus to introduce AI-centric projects: Project Motoko, a smart glasses alternative, and Project AVA, an AI
robotIoTAIautonomous-vehiclesroboticssmart-devicesenergy-efficient-computingUnconventional AI confirms its massive $475M seed round
Unconventional AI, a startup founded by Naveen Rao, former head of AI at Databricks, has secured $475 million in seed funding at a $4.5 billion valuation. The funding round was led by Andreessen Horowitz and Lightspeed Ventures, with additional investments from Lux Capital and DCVC. This initial raise is part of a larger planned round that could reach up to $1 billion. Although the final valuation is slightly below the $5 billion Rao initially aimed for, the company’s value may increase if the full funding target is met. The startup aims to develop a new, energy-efficient computer specifically designed for AI applications, with Rao emphasizing a goal to achieve efficiency comparable to biological systems. Rao has a strong track record in AI and machine learning startups, having previously founded MosaicML, acquired by Databricks for $1.3 billion, and another machine learning platform acquired by Intel for over $400 million. Unconventional AI’s ambitious funding and vision position it as
energyAI-hardwareenergy-efficient-computingstartup-fundingsemiconductor-technologymachine-learningcomputer-architectureCortical Labs' CL1 turns living neurons into programmable processors
Cortical Labs, led by neuroscientist Brett Kagan, has developed the CL1, the world’s first commercial biological computer that uses 800,000 lab-grown human neurons reprogrammed from skin or blood samples to process information. Unlike traditional silicon-based computers, the CL1’s living neurons can learn, adapt, and in some cases outperform machine learning systems. The device, which began shipping in summer 2025 at $35,000 per unit, includes a custom life-support system for the neurons and operates with significantly lower energy consumption compared to conventional data centers. Early users span various fields, including pharmaceutical research, finance, game development, and AI science. The CL1 evolved from an earlier proof-of-concept project called DishBrain, which demonstrated the feasibility of using living neurons for computation by enabling them to play the game Pong. Transitioning from DishBrain to a commercial product required extensive engineering efforts to ensure scalability, reproducibility, and robustness beyond tightly controlled laboratory conditions. Cortical
biological-computingsynthetic-intelligenceneural-networksbrain-computer-interfaceenergy-efficient-computingbiocomputersneuroscience-technologyCortical Labs' CL1 turns living neurons into programmable processors
Cortical Labs, led by neuroscientist Brett Kagan, has developed the CL1, the world’s first commercial biological computer that uses 800,000 lab-grown human neurons reprogrammed from skin or blood samples to process information. Unlike traditional silicon-based processors, these living neurons can learn, adapt, and in some cases outperform machine learning systems. The CL1, priced at $35,000 and shipping since summer 2025, includes a custom life-support system for the neurons and operates with significantly lower energy consumption compared to conventional data centers. Its early adopters span diverse fields such as pharmaceuticals, finance, gaming, and AI research. The journey from the initial scientific proof of concept, DishBrain, to the commercial CL1 product took about two and a half to three years and involved extensive engineering challenges. Moving beyond lab-scale experiments required building a scalable, reproducible system, which meant developing everything from low-level code and kernel-level software to custom hardware including FPGAs and printed
biological-computingsynthetic-intelligenceneural-networksbrain-computer-interfaceenergy-efficient-computingregenerative-medicineAI-researchUS engineers build transistor-like switch for quantum excitons
University of Michigan engineers have developed the first transistor-like switch that can control the flow of excitons—quantum quasiparticles that carry energy without charge—at room temperature. Excitons form when light excites electrons in semiconductors, creating electron-hole pairs that move together as neutral energy packets. Unlike electrons, excitons do not generate heat through energy loss, making them promising candidates for more efficient computing technologies. The team overcame a major challenge by designing a nanostructured ridge that guides excitons along a controlled path and using electrodes as gates to switch exciton flow on and off, achieving an on-off switching ratio above 19 decibels. This breakthrough opens the door to excitonic circuits that could significantly reduce energy consumption and heat generation in computing systems, addressing current limitations faced by electronics in AI and other demanding applications. The researchers also demonstrated an optoexcitonic switch using light to propel excitons rapidly along the ridge, suggesting potential for faster and cooler data transfer in devices
quantum-excitonsexcitonicsnano-switchenergy-efficient-computingsemiconductor-technologyoptoelectronicssolar-cellsLiving cell-based computing system could advance medical biosensors
Researchers at Rice University, supported by a $1.99 million National Science Foundation grant, are developing a novel biological computing system that uses engineered bacterial cells as digital processors. This four-year project aims to create networks of microbes that communicate chemically or electrically, forming parallel computing systems capable of learning, adapting, and responding to environmental inputs. By integrating these microbial networks with electronic systems, the team hopes to build living computers that can perform complex computations with greater energy efficiency than traditional silicon-based hardware. This approach builds on the broader field of biocomputing, which leverages living matter—such as brain organoids or microbes—to overcome the high energy demands of artificial intelligence. Unlike existing efforts like the Swiss company FinalSpark’s organoid-powered AI platform, the Rice project uniquely focuses on microbes, exploiting their natural communication abilities to create adaptable, pattern-recognizing biosensors. Potential applications include advanced medical diagnostics and environmental monitoring, where living biosensors could detect chemical markers and transmit data electronically. The project also plans
biocomputingsynthetic-biologybiological-computingenergy-efficient-computingmicrobial-processorsbioelectronicsAI-energy-solutionsEstonian engineers turn $9 trash phones into pocket-sized data centers
Researchers at the University of Tartu’s Institute of Computer Science in Estonia have repurposed discarded 15-year-old smartphones into low-cost, pocket-sized data centers capable of outperforming popular single-board computers like the Raspberry Pi. By removing batteries from old Google Nexus phones, fitting them with 3-D-printed holders, and powering them externally, the team created clusters costing about €8 (US$9.20) per phone. These clusters run a Linux-based system (PostmarketOS) instead of Android, enabling direct hardware control and enhanced security. The phones, linked as a “master” and “worker” nodes, handle tasks such as AI-powered image recognition and website hosting, demonstrating efficient, high-energy processing in a compact form. The project addresses the environmental issue of e-waste, as billions of smartphones are discarded annually, with most components not properly recycled. By extending the functional life of obsolete devices, the researchers aim to reduce landfill waste and the environmental impact of building new servers.
IoTedge-computinge-waste-recyclingenergy-efficient-computingsmartphone-clustersAI-image-recognitionsustainable-technologyWorld’s first fault-tolerant quantum PC from IBM to launch by 2029
IBM plans to launch the world’s first large-scale, fault-tolerant quantum computer, named Quantum Starling, by 2029. This system will feature 200 logical qubits capable of performing over 100 million quantum operations, representing a 20,000-fold increase in operational capacity compared to current quantum computers. Starling will be developed at a new IBM Quantum Data Center in Poughkeepsie, New York, and will serve as the foundation for a more advanced system, Quantum Blue Jay, which aims to have 2,000 logical qubits and execute one billion operations. The development of fault-tolerant quantum computers hinges on creating logical qubits from clusters of physical qubits to detect and correct errors, enabling large-scale quantum computations without faults. IBM is advancing this goal through innovations such as quantum low-density parity check (qLDPC) codes, which significantly reduce the number of physical qubits needed for error correction by about 90% compared to other methods. IBM’s roadmap also includes intermediate milestones like the Quantum Loon processor (testing qLDPC components in 2025), Quantum Kookaburra (a modular processor integrating quantum memory and logic in 2026), and Quantum Cockatoo (linking Kookaburra modules into a networked system by 2027). These efforts aim to unlock practical, scalable quantum computing with applications in drug discovery, materials science, and chemistry.
quantum-computingIBMfault-tolerant-quantum-computerlogical-qubitsquantum-operationsmaterials-researchenergy-efficient-computing