Articles tagged with Computer Processing
Devadas pioneered the field of hardware security, making foundational contributions to Trusted Execution Environments (TEEs), Physical Unclonable Functions (PUFs), and Side Channel Resistant Processors. His innovations have shaped both academia and industry, safeguarding data in a digital world.
MIT researchers developed a new system-on-a-chip called Gleanmer, which generates highly accurate 3D maps of the robot's environment using Gaussians to represent obstacles. This approach reduces memory and power consumption by up to 99%, making it suitable for lightweight augmented reality headsets.
A new AI model has revolutionized molecular simulations, enabling researchers to predict molecular behavior and identify promising drug candidates more quickly. By analyzing over 12,500 organic molecules, the model has demonstrated accuracy and consistency with physical laws.
Researchers developed a new magnetic memory material that can be rewritten using laser light, allowing for faster and more energy-efficient storage and processing of information. This breakthrough could help reduce power consumption in data centers and support future high-speed information systems.
A team of researchers from The University of Osaka has developed a new approach for depth reconstruction from defocus, estimating distances by analyzing blur in an image. Their method combines a coded-aperture camera with diffusion-model-based AI to accurately estimate depth and produce high-quality images.
Researchers developed an all-optical artificial synapse that uses light to mimic neural learning and perform in-sensor image processing. The device shows paired-pulse facilitation and depression, allowing it to both enhance and suppress signals, a requirement for realistic neural behavior.
A robotic hand developed at USC can hear a melody once and play it back after just two minutes of self-taught practice on a keyboard. The system, called the Musician Hand, mimics the way the brain and body coordinate fine motor skills through trial and error, offering a new model for machines — and medicine — to approach complex moveme...
Experts warn that AI-enhanced surgical robotics could enable true personalized surgery and enhance surgical team performance. However, regulatory reforms are needed to address risks from adaptive systems and ensure patient benefits.
A new study by the University of East London found that newer blockchain systems significantly reduce energy consumption, from 100-150 TWh per year for Bitcoin to negligible amounts. This shift has already led to reductions in network energy use and enables wider adoption at scale.
MIT researchers have created an 'EnergAIzer' method that generates reliable results in seconds, allowing data center operators to optimize resource allocation and reduce energy waste. The tool leverages patterns from AI workloads and software optimizations to provide fast but accurate power estimates.
Researchers at Princeton University have developed a 3D device that combines living brain cells with advanced electronics to recognize patterns using computational techniques. The device creates a vast 3D network of neurons that can be used for computation, offering a potential solution to the energy bottleneck in modern AI technology.
Artificial synapses are built from soft, bio-friendly materials that operate like human brain synapses, merging data storage and computing into a single unit. Laboratory prototypes demonstrate immense capabilities, consuming energy on the scale of femtojoules.
Ricardo Baeza-Yates has made fundamental contributions to computer science through his pioneering work in algorithms and information retrieval. He has fostered a vibrant transnational research community across Latin America, inspiring young people with the South American Programming Contest.
Researchers developed a new AI-assisted tool called CacheMind to improve cache performance and reduce evictions. The tool uses causal reasoning to analyze fine-grained details about system behavior, enabling computer architects to identify patterns and implement fixes.
The AUMOVIO-NTU Corporate Lab will focus on areas like AI, sustainability, novel materials, and connectivity to develop solutions for safer, smarter, and more sustainable transportation. The joint lab aims to accelerate innovations that can support Singapore's future mobility ecosystem.
A new study proposes a learning architecture that integrates educational philosophy with AI-driven design, aiming to transform assessment into an ongoing process of reflection. The system prioritizes human judgment and interpretation over standardized metrics, enabling educators to build adaptive and interpretable feedback systems.
Researchers developed an AI model to create highly photorealistic 3D reconstructions of ground-level damage after earthquakes. The LoRA-Enhanced Ground-view Generation diffusion model can recognize complex visual patterns and predict where structures may be damaged, even in densely populated urban areas.
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.
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 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.
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 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.
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.
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 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.
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.
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.