Power/Performance Bits: Jan. 14


Optical memory Researchers at the University of Oxford, University of Exeter, and University of Münster propose an all-optical memory cell that can store more optical data, 5 bits, in a smaller space than was previously possible on-chip. The optical memory cell uses light to encode information in the phase change material Ge2Sb2Te5. A laser causes the material to change between ordered and... » read more

Power/Performance Bits: Jan. 8


Ferrimagnetic memory Engineers at the National University of Singapore, Toyota Technological Institute, and Korea University propose a new type of spintronic memory that is 20 times more efficient and 10 times more stable than commercial ones. In spintronic devices, data is stored depending on up or down magnetic states. Current devices based on ferromagnets, however, suffer from a few issu... » read more

Power/Performance Bits: Jan. 2


High-temp electronics Researchers at Purdue University, UC Santa Cruz, and Stanford developed a semiconducting plastic capable of operating at extreme temperatures. The new material, which combines both a semiconducting organic polymer and a conventional insulating organic polymer could reliably conduct electricity in up to 220 degrees Celsius (428 F). "One of the plastics transports the ch... » read more

Power/Performance Bits: Dec. 26


2nm memristors Researchers at the University of Massachusetts Amherst and Brookhaven National Laboratory built memristor crossbar arrays with a 2nm feature size and a single-layer density up to 4.5 terabits per square inch. The team says the arrays were built with foundry-compatible fabrication technologies. "This work will lead to high-density memristor arrays with low power consumption fo... » read more

Power/Performance Bits: Dec. 18


Solar storage Engineers at MIT, Georgia Institute of Technology, and the National Renewable Energy Laboratory designed a system to store renewable energy in vast amounts and deliver it back to the grid when power generation is low. The system stores excess electricity from solar or wind installations as heat using tanks of white-hot molten silicon, and then converts the light from the glowi... » read more

Power/Performance Bits: Dec. 11


Internet of Ears for smart buildings Scientists at Case Western Reserve University proposed a new way for smart homes to determine building occupancy: sensors that 'listen' to vibration, sound, and changes in the existing ambient electrical field. "We are trying to make a building that is able to 'listen' to the humans inside," said Ming-Chun Huang, an assistant professor in electrical engi... » read more

Power/Performance Bits: Dec. 4


Bio-hybrid fungi Researchers at Stevens Institute of Technology combined a white button mushroom, electricity-producing cyanobacteria, and graphene nanoribbons into a power-generating symbiotic system. "In this case, our system - this bionic mushroom - produces electricity," said Manu Mannoor, an assistant professor of mechanical engineering at Stevens. "By integrating cyanobacteria that ca... » read more

Power/Performance Bits: Nov. 27


Hybrid solar for hydrogen and electricity Researchers at the Lawrence Berkeley National Laboratory developed an artificial photosynthesis solar cell capable of both storing the sun's energy as hydrogen through water splitting and outputting electricity directly. The hybrid photoelectrochemical and voltaic (HPEV) cell gets around a limitation of other water splitting devices that shortchange... » read more

Power/Performance Bits: Nov. 20


In-memory compute accelerator Engineers at Princeton University built a programmable chip that features an in-memory computing accelerator. Targeted at deep learning inferencing, the chip aims to reduce the bottleneck between memory and compute in traditional architectures. The team's key to performing compute in memory was using capacitors rather than transistors. The capacitors were paire... » read more

Power/Performance Bits: Nov. 13


ML identifies LED material Researchers at the University of Houston created a machine learning algorithm that can predict a material's properties to help find better host material candidates for LED lighting. One recommendation was synthesized and tested. The technique, a support vector machine regression model, was efficient enough to run on a personal computer. It scanned a list of 118,28... » read more

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