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3 Emerging Technologies: Memristors, Spintronics & 2D Materials


New technical paper titled "Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware" from researchers at University College London and University of Cambridge. Abstract "In a data-driven economy, virtually all industries benefit from advances in information technology—powerful computing systems are critically important for rapid technological pr... » read more

Differentiable Analog Nonvolatile CAM (dCAM) Using Memristors


Technical paper titled "Differentiable Content Addressable Memory with Memristors" from researchers at Hewlett Packard Labs and University of Hong Kong. Abstract "Memristors, Flash, and related nonvolatile analog device technologies offer in-memory computing structures operating in the analog domain, such as accelerating linear matrix operations in array structures. These take advantage of ... » read more

Neurosynaptic Device That Mimics Synaptic and Intrinsic Plasticity Concomitantly In a Single cell


New academic paper titled "Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse" from researchers at Korea Advanced Institute of Science and Technology (KAIST). Abstract Neuromorphic computing targets the hardware embodiment of neural network, and device implementation of individual neuron and synapse has attracted considerable attention. The emulation of... » read more

The development of integrated circuits based on two-dimensional materials


Abstract Two-dimensional (2D) materials could potentially be used to develop advanced monolithic integrated circuits. However, despite impressive demonstrations of single devices and simple circuits—in some cases with performance superior to those of silicon-based circuits—reports on the fabrication of integrated circuits using 2D materials are limited and the creation of large-scale circu... » read more

Power/Performance Bits: Jan. 26


Neural networks on MCUs Researchers at MIT are working to bring neural networks to Internet of Things devices. The team's MCUNet is a system that designs compact neural networks for deep learning on microcontrollers with limited memory and processing power. MCUNet is made up of two components. One is TinyEngine, an inference engine that directs resource management. TinyEngine is optimized t... » read more

Power/Performance Bits: Dec. 7


Logic-in-memory with MoS2 Engineers at École Polytechnique Fédérale de Lausanne (EPFL) built a logic-in-memory device using molybdenum disulfide (MoS2) as the channel material. MoS2 is a three-atom-thick 2D material and excellent semiconductor. The new chip is based on floating-gate field-effect transistors (FGFETs) that can hold electric charges for long periods. MoS2 is particularly se... » read more

Power/Performance Bits: Oct. 27


Room-temp superconductivity Researchers at the University of Rochester, University of Nevada Las Vegas, and Intel created a material with superconducting properties at room temperature, the first time this has been observed. The researchers combined hydrogen with carbon and sulfur to photochemically synthesize simple organic-derived carbonaceous sulfur hydride in a diamond anvil cell, which... » read more

Neural Networks Without Matrix Math


The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

Power/Performance Bits: June 2


Neuromorphic memristor Researchers at the University of Massachusetts Amherst used protein nanowires to create neuromorphic memristors capable of running at extremely low voltage. A challenge to neuromorphic computing is mimicking the low voltage at which the brain operates: it sends signals between neurons at around 80 millivolts. Jun Yao, an electrical and computer engineering researcher at ... » read more

The Good And Bad Of 2D Materials


Despite years of warnings about reaching the limits of silicon, particularly at leading-edge process nodes where electron mobility is limited, there still is no obvious replacement. Silicon’s decades-long dominance of the integrated circuit industry is only partly due to the material’s electronic properties. Germanium, gallium arsenide, and many other semiconductors offer superior mobili... » read more

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