Arnaud Vanderroost’s Post

#China’s scientists built a NEW type of tensor processing unit (TPU) — a special type of NANO chip — using carbon nanotubes instead of a traditional silicon semiconductor. Opening doors to energy efficient #AI. America and West🤡🤡Go sanction yourselves😆 For those who haven’t quite grasped reality yet and are living in ignorant bliss and delusion; you really think #China world #techjuggernaut and planet’s ONLY #tier1civilisation cares one iota about tiny TSMC or Samsung? Really🤣 A nation of 1,4 billion can’t best some tiny no name non-sequitur companies? Perhaps believing in America’s and West’s propaganda and lies and who have no worthy tech to speak of who rely on said infinitesimally small companies to remain relevant in the 21st century, is enough for you to keep your head nicely immersed in the sand. But China? Wow, do tell us the colour of the sky on your planet because you sure aren’t on Earth.🤡🤡🤡 https://lnkd.in/gn2pmBpj

Researchers have developed a novel microchip powered by light, departing from traditional electrical methods. According to the researchers, this technology harbors the capability to significantly enhance the speed and efficiency of training future artificial intelligence (AI) models compared to current components. By harnessing photons instead of electrons for intricate calculations, the chip could surpass the constraints of conventional silicon chip structures, leading to substantial boosts in computer processing speed and energy efficiency. Utilizing photons presents numerous advantages over electrons. Primarily, photons travel faster than electrons, as electrons cannot attain the speed of light. Even though electrons can approach such velocities, achieving this would demand an exceptional, and impractical, energy supply. Therefore, employing light would entail significantly lower energy consumption. Additionally, photons lack mass and do not generate heat in the manner that electrons carrying an electrical charge do. Effectual Services #Effectualservices #PhotonChip #AICircuitry #LightDrivenAI #PhotonComputing #AIInnovation #PhotonAI #ChipTech #PhotonicsInAI #LightPoweredAI #PhotonProcessor #QuantumChip #PhotonDrivenComputing #AIPhotonics https://lnkd.in/g-nj6bFG.

Today's computing technologies, based on silicon electronics, are gradually reaching their physical limits. Research into computing devices based on alternative physical principles is becoming increasingly relevant in this context. Recently, I realized how radically #photonic systems could change the traditional approaches to solving complex computational problems. We already use #light to transmit information, but what about processing it? In electronics, specific physical effects and material properties are used to create logic gates and switches, which allow us to build computing machines. The materials used in photonics also have the necessary physical effects. In particular, electro-optical and refractive effects are the basis for creating gates. These effects involve changing the optical properties of materials under the influence of an electric field or light flow, enabling light control. Refractive and photochromic effects can be used as activation functions for photonic neurons due to the threshold changes in material transparency, depending on the intensity of the light flow. Moreover, materials whose transparency can be controlled discretely over a wide range can be weights for an optical neuron. In addition, the phenomenon of interference can be used for matrix computations, which is a classic example of how modern artificial neural networks function. Thanks to these effects and the fact that light, compared to electricity, generates less heat in transparent media, photonic systems can be more energy-efficient, and data transmission and processing can be faster due to high channel density. Breakthroughs in this field will lead to another revolution in AI, making its development and usage more environmentally friendly than GPU-based computing by reducing water and energy consumption needed for cooling computing devices. Already, several startups are advancing photonic computing technologies that cover a wide range of applications, from optimizing AI computations (#Lightmatter, #Lightelligence, #CelestialAI) to quantum computing (#Xanadu, #QuixQuantum, #PsiQuantum) and encrypted computations (#Optalysys). Thinking about how photonics could change our lives is inspiring and creates a desire to dive deeper into the subject. Photonic computing has the potential to revolutionize #AI and increase computational efficiency overall. Being part of the development of such systems is a chance to be at the forefront of technology and be part of progress that leads humanity on the path of discovery.

🌟 Exciting Announcement! 🌟 I was introduced to nanotechnology and the revolutionary advancements it promises through MIT Technology Review articles, and I was intrigued by its potential impact on the world of computing. I began researching more, and I came across the fascinating realm of Chiplets—a groundbreaking new processing technology that could replace traditional processors entirely. I invite you to join me on this journey of exploration and innovation as I navigate the exciting world of nanotechnology! #Chiplets #Technology #Computing #Nanotech Everyday electronics rely on processors, yet they can struggle with complex tasks. Chiplets offer a modular, parallel processing approach, enhancing efficiency and paving the way for future innovation. https://lnkd.in/gGZ99-kh

Chiplets We are regularly watching updates related to AI algorithms and models which are released in the market by technology leaders. Last week, OpenAI has released o1. But, AI cannot survive without its other half, the chips. There is a lot of development happening on the other half, too. We have already heard about: > AIMl optimized chips > Quantum computing > Neuromorphic computing > Distributed computing, etc... But there are other developments happening in the Semicondutor Industry which is worth watching such as 1) Spintronics - Apart from the charge of the electrons, efforts are made to use the spin of the electrons and define 1's and 0's based on the spin whether it is spin up or spin down. This can deliver the same compute faster and with less heat. 2) Orbitronics - Apart from the spin, the electrons also orbit the nucleus. Research is in progress to utilize the orbiting nature of electrons. 3) Optical computing- Instead of electric current, light is used to control the gates which is much faster than the current electric signal. 4) Replacing the wafer materials from Silicone to Graphene or Carbon nanotubes. 5) Instead of one Chip (SoC - System on Chip), efforts are now made to come up with Chiplets which is a modular concept. Individual chips like IO Chiplet, memory Chiplet, compute chiplets are placed side by side on a common substrate. This is specifically helpful for the SDV - software defined vehicles ( cars) where new models are released more frequently and enough volume is not there to design and produce SoC for each model. It's for sure that better days are ahead with respect to #semiconductor/ chip related technologies. Just sharing my learning!!!

**Artificial Intelligence: Time for a Quantum Leap with Smarter Semiconductors!** The relentless march toward smarter artificial intelligence demands the blueprint of cutting-edge semiconductor technology. Recent groundbreaking research by scientists from POSTECH and Korea University might just be the breakthrough we've been waiting for. They’ve developed next-generation Electrochemical Random Access Memory (ECRAM) devices, poised to revolutionize how AI systems function by enhancing computational performance and energy efficiency. Analog hardware, a focal point of this research, offers unprecedented advantages over its digital counterpart, excelling at specific computational tasks and continuous data processing while operating at low power. This novel approach facilitates operations through ion movement and concentration, resulting in a three-terminal structure that separates reading and writing data. With a successful fabrication of these devices in a 64×64 array using the Tiki-Taka algorithm, the potential for commercializing this technology is enormous. Imagine AI neural networks training computations with accuracy and efficiency that digital technologies can only dream of! It's high time we pivot towards technologies that not only advance AI but also ensure eco-conscious energy efficiency. The future of AI is analog, and the future is now! 🔍 Curious about safeguarding your investments in an era of such technological strides? Look no further than Sprott Money for secure and reliable options. Discover more here: [Sprott Money](https://lnkd.in/gY8b-CxP). #AIRevolution #SmartTech #Semiconductors #Innovation #AnalogComputing #FutureOfAI #SprottMoney #SilverSqueeze

💡 The pace of development in AI is breathtaking. Researchers in China have now developed two extremely energy-efficient AI chips; one is optimized for speech recognition, the other for epileptic seizure detection and has world-leading energy consumption. These technological breakthroughs could not only change the AI landscape, but also influence the race between the US and China in the chip sector. What is your assessment of this? ➡ https://lnkd.in/ermUFH8A #Chipwar #AI #ArtificialIntelligence #Research #Electronics #Chip

🗞 Electronic News! 🗞 Researchers in the Netherlands have made a groundbreaking advancement in the field of artificial intelligence by developing a technique that significantly reduces power consumption in AI chips through on-chip training. This innovative approach, spearheaded by experts at Eindhoven University of Technology, eliminates the need to transfer trained models to the chip, paving the way for more energy-efficient AI chips in the future. The development utilizes a neuromorphic architecture but has been tailored for mainstream AI frameworks rather than the traditional spiking networks. Training neuromorphic networks has historically been a cumbersome and energy-intensive process, often involving initial training on a computer followed by model transfer to the chip. #electricalengineering #electronics #embedded #embeddedsystems #electrical #computerchips Follow us on LinkedIn to get daily news: HardwareBee - Electronic News and Vendor Directory

"Exciting breakthrough in computing technology! 🖥️💡 Researchers in India have developed a game-changing chip that can perform complex mathematical operations much faster and more efficiently than traditional computers. Here's why this matters: 🔢 Vector-matrix multiplication (VMM) is a fundamental operation in many advanced computing tasks, from artificial intelligence to data analytics. ⏱️ Current methods are slow and energy-intensive, limiting the speed and scale of our computational capabilities. 🚀 This new chip, featuring 1,024 tiny memory devices made with advanced 2D materials, can perform VMM operations in a single step! 💪 Key advantages: Highly efficient: Uses much less power than traditional methods Scalable: Manufactured using wafer-scale processes Versatile: Can store multiple levels of information in each device 🌟 Implications: Faster AI and machine learning algorithms More energy-efficient data centers and mobile devices Potential breakthroughs in scientific simulations and financial modeling This innovation could revolutionize how we process and analyze data, opening up new possibilities in fields ranging from autonomous vehicles to drug discovery. It's a prime example of how advancements in materials science and computer engineering can come together to push the boundaries of what's possible in computing. What areas do you think could benefit most from this technology? Share your thoughts below! 👇 #TechInnovation #ComputerScience #AIAcceleration #FutureOfComputing" https://lnkd.in/d8VeM5K2

Breakthrough CRAM Device Makes AI 1000x More Energy Efficient! 🌟 💡 Researchers at the University of Minnesota Twin Cities have unveiled a revolutionary hardware device called Computational Random-Access Memory (CRAM), which drastically cuts energy consumption for AI applications by a factor of at least 1,000! ⚡️🔋 🔬 Traditional AI methods consume significant power due to the need for data transfers between logic units and memory. CRAM eliminates these energy-intensive transfers by processing data entirely within the memory array. This breakthrough can save up to 2,500 times more energy compared to conventional methods. 🌱 🌐 According to the International Energy Agency, energy consumption for AI is projected to double by 2026, making innovations like CRAM crucial for sustainable AI development. 📈 🛠️ CRAM is built on over 20 years of research led by Professor Jian-Ping Wang, leveraging Magnetic Tunnel Junctions (MTJs) and nanostructured devices, which are already used in various microelectronics systems. This technology represents a fundamental shift from the traditional von Neumann architecture, enabling computation directly within memory cells and eliminating the long-standing computation-memory bottleneck. 🧠💾 📊 CRAM’s energy efficiency and flexibility allow it to be reconfigured to match the performance needs of diverse AI algorithms, utilizing spintronic devices for higher speed and lower energy consumption. The research team is now looking to collaborate with semiconductor industry leaders to scale up and produce hardware that can advance AI functionality. 🤝🔍 Stay tuned for more groundbreaking tech news! Follow our page to be updated about similar innovations. 🌟 🔬 #AI #EnergyEfficiency #TechInnovation #CRAM #UniversityofMinnesota #SustainableTech