Articles tagged with Computer Processing
A new memory technology tames heat at the nanoscale, enabling rapid switching and reducing energy consumption. By stacking alternating layers of conductive and insulating materials, the researchers achieve a 76% reduction in reset energy demand and a 30-fold decrease in data drift.
Researchers at Karlsruhe Institute of Technology (KIT) have developed new ways to address the growing demand for fast and efficient data exchange. They have created photonic microchips and optical bridges that enable the economical production of optical systems for data networks, data centers, and artificial intelligence.
Researchers at Harvard and Georgia Tech have developed RLE-Bench, a benchmark that tests AI coding agents' ability to engineer physical robots. The benchmark contains 48 tasks that test the agent's ability to perform engineering work required to build and operate robotic systems, including control and perception algorithms, designing r...
The 2026 George Michael Memorial HPC Fellowship recipients, Jie Ren, Amit Samanta, and Xuan Wu, are recognized for their innovative work advancing high-performance computing. Their research focuses on GPU-accelerated computing, HPC infrastructure efficiency, and data compression, aiming to improve the performance, sustainability, and e...
A quantum LDPC code with a large minimum distance near 48 protects 4,612 logical qubits using 9,216 physical qubits, reducing hardware overhead. The code's design framework, inspired by classical LDPC codes, allows for strong evidence of a minimum distance and threshold phenomenon.
Researchers propose an AI framework, called interoceptive AI, that uses internal states to inform learning and decision-making in dynamic environments. This approach treats internal conditions as a continuous source of context, influencing what an agent learns, prioritizes, and does.
CEC researchers are working on a project to enable debugging for AI workloads on GPUs and accelerators. They aim to advance AI development by expanding user communities, enabling distributed development, and accelerating analysis tools.
Optical convolution computation enables parallel light propagation and multiplexing for faster and more energy-efficient computing systems. The review organizes the field into two paradigms: definition-based and theorem-based, which leverage mathematical principles to implement convolution operations in the physical domain.
A new, simplified amoeba-inspired computing model streamlines information-processing while enabling physical implementation, breaking volume-conservation law constraints. The model enables diverse materials and physical phenomena, solving combinatorial optimization problems with improved efficiency.
Researchers integrate AI into local monitoring sensors to track ecosystem health in near real-time, reducing delays of months to years. The project enables faster release of accessible flux data, helping scientists understand ecosystem responses to change and inform land management decisions.
The ST-NUS HELIX Corporate Lab aims to develop new generative and embodied AI use cases at the edge through system-to-silicon innovation, reducing energy consumption and improving performance. Researchers will focus on memory-centric architecture, innovative in-memory computing, and scalable compute-and-memory systems.
A simple interaction rule inspired by honeybee colonies enables robot swarms to reach a clear consensus quickly, even when some information is faulty or manipulated. This rule, called cross-inhibition, introduces a brief moment of indecision, allowing the swarm to form a clear majority.
Researchers at UMass Amherst have designed an edge AI system that leverages hyperdimensional computing algorithms and analog in-memory computing hardware to achieve high accuracy and efficiency. The system achieved 95.24% accuracy in language identification while reducing computing resources by 90%.
Researchers at Seoul National University have developed a novel approach using porous triply periodic minimal surface (TPMS) feet and deep reinforcement learning controller, which significantly reduces battery power consumption in quadruped robots. The solution reduces energy consumption by up to 6.2% while maintaining stable locomotion.
A project led by Michela Taufer aims to accelerate AI-driven scientific discovery by enabling secure data sharing across the nation's research infrastructure. The National Science Data Fabric (NSDF) will connect researchers, computing resources, and data repositories, making advanced AI-driven science accessible to all.
USC researchers have been selected for the U.S. Department of Energy's Genesis Mission to harness artificial intelligence for scientific discovery and innovation. Two projects led by USC will explore ways to develop faster and more energy-efficient computing hardware and better understand the natural concentration of critical minerals.
A Tulane University team is using AI to discover new superconductors, which could improve the nation's electrical grid, medical imaging, and quantum computing. The project combines high-fidelity calculations, physics-aware AI, and experimental measurements to accelerate discovery.
Researchers developed a photospike-based TRNG that harnesses unpredictable light-induced electrical charges to generate true random numbers. The device passed all 15 randomness tests and remained stable over millions of cycles, making it suitable for image authentication and deepfake detection.
A new brain-like electronic device consumes very little energy and detects novelties almost instantly, with over 98% accuracy. The device requires roughly 10,000 times fewer computer operations than conventional AI approaches, paving the way for more energy-efficient AI systems.
A study found that forecast error types influence public emotion during disasters, with anxiety and worry being the most common emotions. The researchers suggest that communicating forecast uncertainty effectively could improve public trust and reduce emotional distress during future extreme weather events.
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.
The University of Michigan has successfully implanted the first-in-human Paradromics wireless brain-computer interface, designed to restore communication for patients with difficulty speaking. The clinical trial will focus on the device's long-term safety and assess its ability to restore communication through synthesized text and speech.
Researchers at University of Michigan Health have implanted the first wireless brain-computer interface (BCI) to restore communication in a patient with motor neuron disease. The study, called Connect-One Early Feasibility Study, aims to assess the device's long-term safety and effectiveness in synthesizing text and speech.
Researchers propose a Digital Twin Optical Computing System that reduces dependence on physical hardware for task development. The DT-OCS framework enables offline simulation, training, and optimization of computational tasks, improving research efficiency and application flexibility.
Avishek Choudhury, a WVU researcher, has won the NSF CAREER award to study how healthcare providers' trust in artificial intelligence changes over time. His goal is to humanize algorithms behind AI and improve decision-making quality and patient safety.
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.
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.
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...
Researchers explore how next-gen AI and sensor-rich operating rooms can enable more precise, data-driven, personalized surgery. Advances in multimodal data integration, machine learning, and robotic systems could enhance situational awareness and intraoperative decision-making.
University of Missouri researchers develop organic transistors that process information like biological neural networks, boosting brain-like computing and potentially leading to more energy-efficient artificial intelligence. The approach could lead to significant improvements in tasks such as pattern recognition and decision-making.
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.
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.