Articles tagged with Computer Processing
Researchers developed a new type of nanoelectronic device mimicking the human brain's efficient neuron connections, reducing energy consumption for AI systems. The hafnium-based devices achieve switching currents millions of times lower than conventional devices and store programmed states for around a day.
Scientists have added millions of protein complex structures to the AlphaFold Database, shedding light on how proteins interact. The dataset prioritizes human health and disease research, enabling researchers to test, refine, and build upon it.
The ARU Arm AI Lab will provide researchers and students with access to advanced Arm AI technology, focusing on real-world applications in healthcare and life sciences. This partnership will also support emerging talent and drive innovation, building on existing collaborations and industry projects.
Study by Duke engineers reveals that a common device architecture used to test 2D transistors overstates their performance prospects up to sixfold. The back-gated architecture amplifies the transistor's performance using contact gating, but has physical limitations that prevent its use in commercial technologies.
The Baskerville National Compute Resource (NCR) will benefit researchers in various disciplines with advanced data processing capabilities. The facility harnesses accelerator technology to process vast amounts of data at incredible speed, helping researchers achieve breakthroughs faster than ever before.
Researchers developed open-source software to measure AI model energy use, revealing trends in how design affects power requirements. The tool can automate the search for efficient parameters, helping lower energy costs and environmental impact.
Researchers will gather to discuss how to engineer AI systems that work in the real world, focusing on composition, optimization, verification, and evaluation. The conference aims to establish shared foundations for a new class of software, including methods for evaluating models and ensuring durability, efficiency, and dependability.
A University of Houston professor has found that tree-like thin films release heat at least three times better than traditional methods, enabling more efficient cooling in AI data centers. The discovery demonstrates the power of physics-aware AI design for validating high-impact cooling solutions.
Researchers at the University of Pennsylvania have developed HoloRadar, a system that enables robots to reconstruct hidden 3D spaces beyond their line of sight using radio waves processed by AI. This capability can improve safety and performance in driverless cars and cluttered indoor settings.
NTU Singapore is launching three new space projects under the Space Access Programme to accelerate the commercialization of space technologies. The projects include an AI-enabled satellite, a nanosatellite testing next-generation solar cells, and another nanosatellite with advanced propulsion systems.
A new method called Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) accelerates analysis of hyperspectral data, enabling faster AI-guided discoveries for high-performing crops. The approach reduces computational bottleneck and increases efficiency, making it possible to extract subtle patterns in plant physiology.
Researchers have developed a passive, solar-powered orbital data center that can scale AI computing and reduce environmental impact. The system leverages decades of research on 'tethers' and could host thousands of computing nodes to replicate terrestrial data centers.
Researchers at Politecnico di Milano developed a 'smart' chip that dramatically reduces energy consumption while accelerating data processing, achieving similar accuracy to digital systems with lower power consumption and faster performance.
A reconfigurable optical computing platform based on a double-layer liquid crystal structure has been developed to enable multifunctional all-optical image processing. The platform integrates eight types of image processing functions in one go, including bright-field imaging, vortex filtering and edge enhancement, promising substantial...
Researchers at University of Waterloo discover workaround for 'no cloning' problem in quantum computing by encrypting quantum information as it's copied. This breakthrough enables redundant and encrypted quantum cloud services, a crucial step towards building quantum computing infrastructure.
Researchers developed a bio-inspired neuron platform that processes and learns information using light and electronics integrated on a single platform. The chip achieves 92% image recognition accuracy and demonstrates key synaptic behaviors found in biological learning.
A new study finds that large language models like ChatGPT significantly boost paper production, especially for non-native English speakers. However, this increase in AI-written papers makes it harder to separate valuable contributions from low-quality work.
Researchers propose integrating processing capability within memory units to reduce energy consumption and latency in AI applications. Inspired by the brain's efficient processing mechanisms, spiking neural networks (SNNs) can respond to irregular events and store information in the same place.
Researchers propose a novel approach to AI hardware design by integrating neuromorphic systems and compute-in-memory techniques to overcome the limitations of modern computing hardware. This could lead to more efficient data center energy use and enable real-time intelligence in compact, power-constrained systems.
The UC3M's new supercomputer is a state-of-the-art facility designed to support research and development projects requiring high-performance computing. The system features over 11,500 CPU cores and 42 GPUs, enabling the processing of large volumes of data and complex numerical simulations.
Stanford engineers develop an AI framework called MetaChat, which rapidly accelerates metasurface design using computational tools and self-reflective AI assistants. This enables rapid solving of optics-related problems, such as designing metal lenses that can focus different wavelengths of light.
The Association for Computing Machinery has named 61 new Distinguished Members, recognized for their contributions to AI for healthcare, data management, human-computer interaction, and other areas. The recipients include experts from top universities, corporations, and research institutions worldwide.
The University of Texas at Dallas has partnered with Tech Mahindra to facilitate collaboration on artificial intelligence (AI) innovation, skill development, and research. The partnership will provide opportunities for students and faculty to advance AI technologies, data science, and cybersecurity.
A breakthrough AI system called OmniPredict can predict human pedestrian behaviors with unprecedented accuracy, revolutionizing self-driving cars and urban mobility. The model combines visual cues with contextual information to anticipate pedestrians' next moves, reducing the risk of accidents and improving traffic safety.
The ACM Gordon Bell Prize-winning team developed a full-physics Bayesian inversion framework called 'digital twin' to predict tsunami early warning. This approach enabled real-time, data-driven forecasting with dynamic adaptivity, achieving a ten-billion-fold speedup over existing state-of-the-art methods.
A University of Delaware team, funded by NSF, creates next-generation secure computer chips using AI and game theory. The project aims to predict attacks and set new benchmarks for global hardware security, enabling the design of safer and more trustworthy systems.
Researchers have demonstrated single-shot tensor computing at the speed of light, a breakthrough step towards next-generation AI hardware. This method uses optical computation to perform complex tensor operations, enabling fast and low-power processing.
A small-scale neuromorphic computer prototype learned patterns and made predictions using fewer training computations than conventional AI systems. The prototype, developed by the University of Texas at Dallas team, integrates memory storage with processing, allowing for more efficient AI operations with lower costs.
A new study finds that an AI-powered lifestyle intervention is noninferior to human coaching in achieving a composite outcome in adults with prediabetes and overweight or obesity. The AI-led Diabetes Prevention Program was noninferior to the human-led program in terms of weight reduction, physical activity, and HbA1c levels.
Researchers from Ohio State University have developed shiitake-based devices that can act as organic memristors, a type of data processor. These devices demonstrated similar reproducible memory effects to semiconductor-based chips and showed potential for creating low-cost, environmentally friendly brain-inspired computing components.
Amarasinghe recognized for groundbreaking work in domain-specific languages and exceptional mentorship, advancing the global computing community. His contributions have successfully bridged the gap between software and hardware to fully exploit modern hardware resources.
A recent study reveals that the response to witnessing robot mistreatment depends on factors such as the robot's humanlike design and the observer's moral identity. Anthropomorphism influences empathy, encouraging customers to treat robots with dignity, while moral identity plays a crucial role in shaping behavioral contagion.
Researchers at Kyushu University have developed a new method to build more energy-efficient magnetic random-access memory (MRAM) using thulium iron garnet. The team successfully produced thin films of platinum on the TmIG material, enabling high-speed and low-power information rewriting at room temperature.
Researchers at the University of Sydney have developed end-to-end encryption for git services, protecting sensitive code from cybersecurity threats. The encryption aligns seamlessly with existing platforms like Github and Bitbucket, reducing computational resources needed while encrypting only edits.
Scientists have developed a programmable electronic circuit that harnesses high-frequency electromagnetic waves to perform complex parallel processing at light-speed. This breakthrough has the potential to power next-generation wireless networks, real-time radar, and advanced monitoring in various industries.
Ana Veroneze Solórzano recognized for broadening HPC's societal impact with privacy-preserving mechanisms, while Yafan Huang advances exascale computing with ultra-fast compression algorithms. Both receive fellowships to support their research on high-performance computing applications.
Researchers developed a platform called CRESt that incorporates insights from literature, chemical compositions, and imaging to optimize materials recipes. CRESt uses robotic equipment for high-throughput testing and large multimodal models to further optimize materials recipes.
Qiong Ma, Assistant Professor of Physics at Boston College, has been selected as a 2025 Moore Inventor Fellow for her groundbreaking work on twistronic artificial synapses. The fellowship award comes with $675,000 over three years and will support the purchase of new scientific equipment and funding for postdocs and student researchers.
Researchers developed MoBluRF, a two-stage motion deblurring method for NeRFs, achieving high-quality 3D reconstructions from ordinary blurry videos. The framework outperforms state-of-the-art methods and is robust against varying degrees of blur, enabling smartphones to produce sharper and more immersive content.
The University of Texas at San Antonio has launched the College of AI, Cyber and Computing, which will serve over 5,000 students pursuing degrees in rapidly growing career fields. The college is positioned to advance technological education and research, with faculty securing major grants and launching industry partnerships.
Researchers at Politecnico di Milano developed photonic chips for training physical neural networks, eliminating digitisation requirements. This allows for faster, more robust, and efficient network training using light signals.
A new study from Rice University introduces a novel solar thermal-boosted organic Rankine cycle (ORC) system that can recover 60-80% more electricity annually from waste heat in data centers. The approach achieves over 8% higher ORC efficiency during sunny peak hours and lowers the cost of electricity by 5.5-16.5%.
A team of researchers developed a method to annotate biopsy image data with eye-tracking devices, reducing the burden on pathologists. The resulting AI model achieved an accuracy of 96.3% and surpassed human pathologists' performance in diagnosing skin lesions.
The system tracks and analyzes crop development using data from sensors, biosensors, the Internet of Things, and AI. Strong security protocols ensure farmer data remains private and resilient against future quantum computer attacks. The research team plans to improve their system with faster sensor processing and a solar-powered battery.
This book provides a beginner-friendly resource on the impact and evolution of decentralized networks, highlighting their applications in healthcare, supply chains, agriculture, climate monitoring, and education. The authors emphasize sustainability, data security, and ethical tech adoption.
A new study reveals that pedestrians are now walking faster and spending less time in public spaces. Researchers analyzed 40 years of video footage to find a 14% decline in people lingering in these areas.
The team aims to deliver AI power directly to devices, improving resilience and speed in constrained environments. By processing data step-by-step across a network of devices, they can create a safe and adaptable system that can withstand attacks and extreme conditions.
Researchers use AI to solve differential equations, such as Schrodinger's equation, for large-scale systems, improving efficiency and accuracy in fields like drug discovery and material design.
A new robotic slip-prevention method has been developed to improve robots' grip and handling of fragile or slippery objects. This bio-inspired approach allows robots to predict when an object might slip and adapt their movements in real-time, outperforming traditional strategies.
Researchers developed an AI-based screening tool using pangrams to detect Parkinson’s disease with nearly 86 percent accuracy. The web-based test analyzes voice recordings for subtle patterns linked to the neurodegenerative disease, identifying potential warning signs.
The University of Texas at Arlington has launched a $2.1 million supercomputing hub to support data-intensive research in AI, imaging, and energy modeling across campus. This new resource will enable researchers to process and analyze large-scale datasets, leading to significant discoveries across disciplines.
MIT engineers developed a versatile demonstration interface that allows users to teach robots new skills in three intuitive ways: remote control, physical manipulation, or demonstration. This innovation expands the type of users and 'teachers' who interact with robots, enabling robots to learn a wider set of skills.
The new compute roadmap aims to improve lives and livelihoods with advanced computing power. It includes investments in AI research, healthcare diagnostics, and renewable energy.
Researchers from Université Laval designed an ultra-fast and greener optical chip that can transfer massive amounts of data at speeds of 1,000 gigabits per second while reducing energy consumption. This innovation uses the phase of light to add a new dimension to the signal, reaching unprecedented performance levels.
The new journal aims to accelerate knowledge dissemination in AI by prioritizing theoretical breakthroughs, practical applications, and societal implications. It features short peer-reviewed contributions and opinion pieces to facilitate swift engagement with contemporary AI issues.
Researchers at Cornell University have developed a process that transforms short videos of rooms into highly accurate, interactable 3D simulations. The technology can be used to create more realistic video games and train robots to operate within specific real-world spaces.
Cassie, a digital-human assistant developed by Texas A&M University, is transforming the way patients interact with healthcare providers. With facial recognition and emotional intelligence, Cassie offers a two-way interaction that feels like a conversation.
A research team developed AI technology that analyzes individual personality traits and values to generate personalized analogies, allowing people to understand others' feelings through familiar experiences. This approach significantly improved emotional understanding and empathy in participants compared to traditional methods.
University of Missouri researchers create digital sentiment map using AI to analyze public Instagram posts, linking emotional tone to real-life features. The tool aims to improve city services, identify areas of concern, and inform emergency response decisions.
Researchers at Pohang University of Science & Technology have developed Pixel-Based Local Sound OLED technology, allowing each pixel to emit different sounds. This breakthrough enables truly localized sound experiences in displays, enhancing realism and immersion.