Articles tagged with Computer Memory
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.
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.
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 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.
A new international project aims to protect fragile quantum information from decoherence and loss, a key barrier to quantum computing's progression. The Magenium qubit design stores information in small, symmetric clusters of qubits, potentially allowing quantum data to last significantly longer than current methods.
Researchers at the University of Minnesota have discovered a way to manipulate charge flow in ultrathin metallic films using light. This breakthrough could lead to energy-efficient optical sensors, detectors, and quantum information devices.
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 at the University of Minnesota have developed a new material called Ni₄W that can generate spin currents to control magnetization in electronic devices. This material has the potential to significantly reduce power usage in devices like smartphones and data centers.
Researchers have developed a new way to precisely tune magnetism using ultra-thin CrPS₄ material. This breakthrough could solve long-standing scientific problems and pave the way for smarter magnetic technologies.
Researchers from The University of Osaka developed a technique to recover magnetization in degraded spintronics devices using molecular hydrogen and Pt underlayers. This method can improve the robustness of semiconductor memory.
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.
Researchers at Washington State University have discovered a hybrid zinc telluride-based material that undergoes structural changes when subjected to pressure, making it a strong candidate for phase change memory. The material's layered structure and directional sensitivity open the door to additional uses in photonics.
Researchers developed new photon avalanching nanoparticles that exhibit high nonlinearities, overcoming challenges in realizing intrinsic optical bistability at the nanoscale. The breakthrough paves the way for fabricating optical memory and transistors on a nanometer scale comparable to current microelectronics.
The UChicago Pritzker Molecular Engineering team has developed a technique to store classical computer memory in crystal gaps where atoms should be, enabling terabytes of data storage in a small millimeter-sized cube. This innovation combines quantum techniques with solid-state physics to revolutionize classical non-quantum computers.
Dr. Ted Moise, UT Dallas professor and director of the North Texas Semiconductor Institute, has been honored as a National Academy of Inventors Fellow for his groundbreaking work on ferroelectric random-access memory (FRAM). This technology enables faster data storage while using less power, with applications in ultra-low power microco...
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.
Researchers developed a fast and scalable programmable photonic latch, enabling temporary data storage in optical processing systems. This technology could enhance AI operations by storing and retrieving data at high speeds.
Researchers developed a groundbreaking photonic platform to overcome limitations in in-memory computing, enabling faster calculations and greater efficiency. The innovative magneto-optical memories consume about one-tenth the power of traditional electronics and can be rewritten billions of times.
Researchers from Osaka University have developed a new technology to lower power consumption for modern memory devices, enabling an electric-field-based writing scheme. The proposed technology could provide an alternative to traditional RAM and is a promising step towards implementing practical magnetoelectric (ME)-MRAM devices.
Researchers create multilayered chip design that doesn't require silicon wafer substrates, allowing for better communication and computation between layers. This breakthrough enables the construction of fast and powerful AI hardware comparable to supercomputers.
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.
A new class of magnetism called altermagnetism has been imaged for the first time, offering potential to increase operation speeds of up to a thousand times in digital devices. Altermagnets combine favorable properties of ferromagnets and antiferromagnets into a single material.
Researchers discovered a key security weakness in AMD computer processors due to 'BadRAM', rogue memory modules providing false information to the processor. This allows attackers to bypass CPU memory protections and access sensitive data or cause disruptions in shared cloud environments.
Researchers developed a heat-tolerant memory device that can store and rewrite information at temperatures over 1100°F, comparable to the surface of Venus and the melting temperature of lead. The device uses oxygen ions instead of electrons, allowing for precise control of voltage states and potential in-memory computing applications.
Technion researchers create new platform, PyPIM, to support in-memory computing. This enables developers to write software directly for in-memory-computing architecture, improving performance and reducing data transfer time.
Researchers developed a new method for amorphizing indium selenide wires, requiring as little as one billion times less power density. The process resembles an avalanche and an earthquake, triggering rapid deformation and linking small areas into larger ones, potentially unlocking wider applications for phase-change memory technology.
Scientists have developed a new method for converting crystal to glass using electric current, reducing the need for high-power melt-quench processes. The discovery could transform data storage in devices and unlock wider applications for phase-change memory technology.
Researchers from Pitt, UC Santa Barbara, University of Cagliari, and Institute of Science Tokyo have developed a new method for photonic in-memory computing that combines non-volatility, multibit storage, high switching speed, low switching energy, and high endurance in a single platform.
Researchers discovered phase separation plays a crucial role in memristors retaining information over time. The team developed a device with improved retention behavior, yielding results comparable to 10 years of storage without power.
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.
Researchers at University of Bath and Technical University of Darmstadt found that large language models like ChatGPT cannot learn independently or acquire new skills, making them controllable and predictable. The study concluded that LLMs remain inherently safe, but misuse is still possible.
A new device called computational random-access memory (CRAM) reduces energy consumption for artificial intelligence applications. CRAM enables true computation in and by memory, breaking down the bottleneck in traditional computing architecture.
Researchers have discovered aluminum scandium nitride (AlScN) films that remain stable and maintain their ferroelectric properties at temperatures up to 600°C, making them promising candidates for next-generation ferroelectric memory devices. The films exhibit a high remnant polarization value and only a slight increase in coercive fie...
A novel mechanical metamaterial, 'Chaco,' exhibits history-dependent behavior, allowing it to remember the sequence of actions performed on it. This property enables potential applications in memory storage and robotics.
Researchers have achieved record speeds of up to 900 m/s by moving magnetic skyrmions using electrical currents. This breakthrough offers new prospects for developing higher-performance and less energy-intensive computing devices.
Researchers visualize chiral interface state at atomic scale for the first time, allowing on-demand creation of conducting channels. The technique has promise for building tunable networks of electron channels and advancing quantum computing.
Researchers at the University of California San Diego developed superconducting loops that can demonstrate associative memory, allowing computers to remember relationships between unrelated items. The technology has significant power savings, with a million times less energy requirement than traditional computing architecture.
Researchers have invented a simple yet effective algorithm called SIEVE to optimize cache management. By labeling objects as 'zero' or 'one', the system efficiently evicts the least recently used items, reducing computational complexity and bugs.
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.
Researchers outline new method to stabilize bulk hafnia in metastable ferroelectric and antiferroelectric states, paving the way for non-volatile memory technology. The approach requires less yttrium, improving material quality and purity.
Researchers developed a novel approach to integrate multiple functions into a single chip using monolithic 3D integration of layered 2D materials. This technology offers unprecedented efficiency and performance in AI computing tasks, enabling faster processing, less energy consumption, and enhanced security.
Researchers at University of Rochester developed a new form of computing memory by straining materials to create hybrid phase-change memristors. This approach combines the benefits of memristors and phase-change materials, overcoming limitations of existing forms of memory.
Gallium oxide-based flash memory device demonstrates high performance and stability in extreme temperatures and radiation, retaining data for over 80 minutes. The team aims to improve device properties through further material quality and design advancements.
Researchers at NTU Singapore have developed a method to read data stored in antiferromagnets, allowing for potential energy-efficient and high-speed computing. This breakthrough could lead to the creation of new memory chips with improved performance and capacity.
A new FE-FET design demonstrates record-breaking performances in computing and memory, achieving large memory window with impressively small device dimensions. The combination of molybdenum disulfide and aluminum scandium nitride materials enables energy-efficient devices for both computing and non-volatile memory applications.
Researchers at Lund University have created ferroelectric 'grains' that control tunnel junctions in transistors, allowing for individual-level control and optimization of material properties. This breakthrough enables the development of new circuit architectures for neuromorphic computing and energy-efficient semiconductors.
Researchers at the University of Cambridge have developed a new type of computer memory that can process data in a way similar to the human brain. This technology uses hafnium oxide and tiny self-assembled barriers to store and process information, enabling greater density, higher performance, and lower energy consumption.
Ferroelectric materials like hafnia show promise for non-volatile random-access memory (RAM) due to their stability at high temperatures. Hafnia's unique properties, including the movement of oxygen vacancies, make it an attractive candidate for memristors that mimic brain-like computer architectures.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.
Researchers at Argonne National Laboratory have discovered ultrasmall swirling magnetic vortices, known as merons and skyrmions, in an iron-containing material. These tiny magnetic structures show promise for future computer memory storage and high-efficiency microelectronics due to their stability and adaptability to binary code.
Researchers discovered a size threshold beyond which antiferroelectric materials become ferroelectric, losing energy storage advantages. At thicknesses below 40 nm, the material becomes completely ferroelectric, while above 270 nm, ferroelectric regions appear.
Scientists at Argonne National Laboratory have discovered tiny magnetic vortices called skyrmions that could store data in computers, promising 100-1000 times better energy efficiency than current memory. The team used AI and a high-power electron microscope to visualize and study the behavior of these micro-scale magnetic structures.
Researchers developed a new technique that enables on-device training using less than a quarter of a megabyte of memory, reducing the need for powerful computers and central servers. This approach preserves privacy by keeping data on the device, making deep learning more accessible for low-power edge devices.
Researchers create textile-based pneumatic computers capable of digital logic, onboard memory, and user interaction. The technology aims to assist people with functional limitations in daily tasks without electricity.
Researchers at Rice University have developed cells that can store and process information similar to computer RAM. The cells will be programmed to synthesize redox-active molecules that carry information to and from the outside world, allowing for quick read and write capabilities.
Scientists have successfully switched the state of a bit in memory using spin-orbit torque switching in antiferromagnetic material Mn3Sn, promising faster and more efficient devices. This breakthrough could lead to radical improvements in performance compared to current electronic devices.
Scientists have found a new phenomenon where an atomic switch has to be switched back and forth four times to return to its original state. The spin of gadolinium atoms performs one full rotation during this process. This discovery opens up possibilities for material physics and could potentially be used to store information.
Researchers at MIT identified a flawed analysis of website-fingerprinting attacks and developed new techniques to prevent them. They found that attackers can use machine-learning algorithms to decode signals leaked between software programs, enabling them to obtain private information.