Articles tagged with Computer Hardware
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.
Researchers say tech companies need to adopt user-centered approaches to create authentication systems that are secure by design. They call for more inclusive and consistent methods, avoiding one-size-fits-all models that burden users with complex security rules.
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.
A UMass Amherst-led study suggests old-school analog computing can improve energy efficiency and computing speed in the IoT. Researchers have developed a brain-inspired sensing system that combines touch sensors and smart memory chips to reduce data transmission speeds.
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.
New research demonstrates neuromorphic computing's ability to solve complex mathematical problems, including partial differential equations. This technology has the potential to revolutionize energy-efficient computing and tackle real-world challenges.
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.
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.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
Researchers at the University of Missouri have developed an AI-powered method to detect hidden hardware trojans in chip designs, offering a 97% accurate solution. The approach leverages large language models to scan for suspicious code and provides explanations for detected threats.
Researchers at NC State University identified a hardware vulnerability that allows attackers to compromise AI privacy by exploiting ML accelerator behavior. The GATEBLEED vulnerability exploits power gating in AI accelerators, creating an observable timing channel for attackers.
Researchers at UBC have developed a new NFC-based gesture system that can distinguish between nine distinct gestures with high accuracy. This technology has the potential to improve accessibility for users with mobility issues and reduce the spread of germs through touchpads.
Researchers at Texas A&M University are working to overcome manufacturing hurdles for micro-LED displays, which use inorganic materials to create more robust and longer-lasting screens. The technology has potential for applications in virtual reality, augmented reality, and flexible displays.
Researchers at Seoul National University of Science & Technology developed LWMalloc, a lightweight and high-performance dynamic memory allocator for resource-constrained environments. The new allocator outperforms ptmalloc by achieving up to 53% faster execution time and 23% lower memory usage.
Researchers at Princeton University have developed a machine-learning system that can shape ultrahigh frequency transmissions to avoid obstacles, allowing for real-time adaptation in dynamic environments. This breakthrough could enable the widespread adoption of sub-terahertz frequencies for high-speed data transmission in applications...
Researchers at the University of Missouri have created a more efficient method for manufacturing computer chips using ultraviolet-enabled atomic layer deposition (UV-ALD). This approach reduces the number of manufacturing steps, saving time and materials, while also minimizing the use of harmful chemicals.
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 developed an AI-powered microscope system to measure soil fungi presence and quantity, providing insights into soil health and fertility. The low-cost optical microscopy with machine learning technology can be used by farmers and land managers worldwide.
Researchers at UMass Amherst created integrated arrays of gate-tunable silicon photodetectors that can capture dynamic visual information and classify static images with high accuracy. The technology has the potential to reduce latency in computer vision tasks, enabling applications like self-driving vehicles and bioimaging.
Researchers at the University of Tokyo have developed a new transistor design using gallium-doped indium oxide, achieving high mobility and reliable performance. The gate-all-around structure enhances efficiency and scalability, making it suitable for big data and AI applications.
The new AI chip uses hyperdimensional computing to recognize patterns without requiring millions of data records. This approach saves energy compared to conventional chips, with the new chip consuming only 24 microjoules for training a sample task.
Researchers from ETH Zurich have discovered a new class of vulnerabilities in Intel processors that can be exploited to gain unauthorized access to information from other users. The 'BPRC' vulnerability affects all Intel processors and can be triggered by special inputs, allowing attackers to read sensitive information.
Skia identifies and decodes shadow branches, storing them in a memory area to alleviate bottlenecks and improve throughput. The technique can lead to quicker performance and less power consumption for data centers.
Sandia Labs is testing laser-based photonic cooling for computer chips, which could significantly lower power consumption and increase efficiency. The technology aims to regulate chip temperatures without using water or air-based systems.
Three UVA Engineering faculty members have been elected as AAAS Fellows for their groundbreaking work in computer architecture, energy transport, and hydrology. Sandhya Dwarkadas, Patrick E. Hopkins, and Venkataraman Lakshmi were recognized for their innovative research and contributions to their respective fields.
A team led by Dr. Marcus Botacin is creating a large language model (LLM) to automatically identify malware and write rules to defend against it. The LLM will use signatures to complement human analysts' skills, identifying malware faster and more accurately.
A study developed by the US Department of Energy's Thomas Jefferson National Accelerator Facility aims to lower data center costs using machine learning. The Digital Data Center Twin (DIDACT) system detects anomalies and diagnoses their source using AI continual learning, reducing downtime for scientists processing data from experiments.
Chalmers University researchers develop new ways to make cache memory work smarter, enabling faster data retrieval and improving computer performance. The innovation is part of the European Processor Initiative aimed at securing European independence in high-performance computing chips.
Neuromorphic computing is poised to emerge into full-scale commercial use, driven by the need for energy-efficient solutions. The review article proposes strategies for building large-scale neuromorphic systems that can tackle complex real-world challenges.
Researchers propose several key features to optimize sparsity, massive parallelism, and hierarchical structure in neural representation for neuromorphic systems. The goal is to achieve energy efficiency and compactness while retaining information at high fidelity.
Jefferson Lab is investing $3 million in 13 proof-of-principle projects to explore new ideas and technologies, including nuclear physics, particle accelerator science, and computational science. The LDRD program aims to foster creativity and exploration of cutting-edge research.
Researchers are developing a system to instantly conceal and anonymize voices through computer-generated speech. The technology will allow users to control factors like age, gender, and dialect in real-time conversations.
A team of researchers aims to improve autonomous vehicle safety by identifying and mitigating vulnerabilities in software and hardware. They plan to use knowledge gained from a $926,737 NSF award to design protection mechanisms that can be applied selectively to ensure safety while maximizing efficiency.
Researchers have demonstrated DNA-based technologies that can store, retrieve, compute, erase, and rewrite data. The technology uses soft polymer materials with unique morphologies to create a structure with high surface area for depositing DNA, enabling the full range of operations found in traditional electronic devices.
Konstantinos Mamouras, assistant professor of computer science at Rice University, has been awarded a $547,555 NSF CAREER Award to decentralize IoT applications and relieve network congestion. He aims to introduce novel programming techniques that enable efficient and reliable IoT development.
Researchers at Graz University of Technology discovered three side-channel attacks on WebGPU browser interface, allowing them to access a computer's cache memory and extract sensitive information. The attacks were fast enough to succeed during normal surfing behavior, highlighting the need for browsers to address GPU security and priva...
Researchers at UMass Amherst have developed an analog computing device called a memristor that can complete complex scientific tasks while reducing energy consumption. The device uses physical laws to perform computations in a massively parallel fashion, accelerating matrix operations and overcoming the limitations of digital computing.
Researchers at Princeton University have developed a new AI chip that can run powerful AI systems using significantly less energy than existing semiconductors. The chip is designed to be compact and efficient, enabling its deployment in dynamic environments such as laptops, phones, and data centers.
Researchers at Stanford University have developed a new phase-change memory that could help computers process large amounts of data faster and more efficiently. The technology improves several metrics simultaneously, including speed, endurance, and stability, while operating below 1 volt.
A team of researchers from Northwestern University and the University of Pittsburgh has received a $600K NSF Award to explore novel learning-enabled cyber-physical systems (LE-CPSs) for building flexibility into hardware. This could lead to extended device lifespan, reduced e-waste, and improved sustainability.
Researchers found a novel method to read data from CPUs by analyzing power consumption, dubbed Collide+Power. This attack consumes power and causes delays, allowing attackers to derive targeted data.
Researchers summarize existing compiler technologies in deep learning co-design and propose a new framework, the Buddy Compiler, to address performance bottlenecks in current AI applications. The study highlights the importance of hardware-software co-design in achieving optimal efficiency and effectiveness in deep learning systems.
Researchers from Radboud University have developed a quantum simulator to create artificial molecules resembling real organic ones. This allows for the simulation of complex chemical reactions and properties, paving the way for new materials and technologies.
A team of researchers has created a touch-responsive fabric armband that can be used as a keyboard or sketchpad. The device uses a pressure-sensitive hydrogel sandwiched between layers of knit silk to interpret user input, allowing for real-time writing and gaming on computers.
Researchers at Ruhr University Bochum detected deviations in 37 out of 40 chips, with a high detection rate achieved through clean room conditions and optimized algorithms. The team released their findings and analysis tools online for further study.
A study of over 700 children aged 8-16 with concussions found that engaging in moderate screen time was associated with faster symptom clearance. The researchers suggest using moderation as the approach to screen time, rather than complete bans, to support concussion recovery.
Researchers at North Carolina State University have developed FAXID, a hardware-based approach for detecting ransomware that is significantly faster than software-based methods. In proof-of-concept testing, FAXID demonstrated accuracy comparable to XGBoost but with speeds up to 65.8 times faster.
Researchers have developed a new encryption technique that leverages hardware and software to improve file system security for next-generation non-volatile memories. This approach allows for faster performance than existing software security technologies, making it suitable for large data centers and cloud systems.
Researchers developed a new reading method for SOT-RAMs that can nullify the readout disturbance, reducing it by at least 10 times. The method involves creating a bi-directional read path, cancelling out the disturbances produced by spin currents.
Researchers at Purdue University have created a device that can dynamically rewire itself to adapt to new data, enabling artificial intelligence to learn and remember information like the human brain. This breakthrough could lead to more efficient AI systems for tasks such as image recognition and decision-making.
A team of researchers from Osaka University has developed a simple system based on electrochemical reactions that can perform complex calculations. The system uses polyoxometalate molecules and deionized water to process information and solve nonlinear problems.
Washington University researchers have designed a new processing-in-memory (PIM) circuit that can increase PIM computing's performance by orders of magnitude. The circuit uses resistive random-access memory PIM, allowing for analog computations and eliminating the need for digital conversions.
Researchers at University of Missouri and University of Chicago develop an artificial material that can respond to its environment, make decisions, and perform actions not directed by humans. The material uses a computer chip to control information processing and convert energy into mechanical energy.
A team of researchers at Aarhus University aims to develop an optical sensor using terahertz light to decode the direction of tiny magnetic 'tornadoes' called skyrmions. Skyrmions offer a promising candidate for future bits in computer technology, requiring less power and generating less heat than current methods.
Researchers from North Carolina State University developed a software toolkit to test Apple device security, identifying a previously unknown vulnerability called iTimed. The team used this toolkit to reverse-engineer key components of Apple hardware and demonstrated the vulnerability's potential impact.
GraphIt, a state-of-the-art graph programming language, has been extended to run on graphics processing units (GPUs), enabling faster graph analysis. This advance accelerates graph algorithms, particularly those that benefit from parallelism, such as recommendation algorithms and internet search functions.
Xiaochen Guo, a Lehigh University professor, aims to improve data movement efficiency by revamping memory systems. Her goal is to proactively create and redefine locality in hardware, unlocking fundamental improvements for machine learning applications.
Researchers from UConn, University of Maryland, and Rice University will analyze and upgrade security protections for nanoscale computer hardware with a $7.5 million grant. The project aims to elevate security as a fundamental design parameter for next-generation nanoscale devices.