Power/Performance Bits: May 21


More speculative vulnerabilities Security researchers at the Graz University of Technology, KU Leuven, Cyberus Technology, and Worcester Polytechnic Institute point to two new speculative execution vulnerabilities related to Meltdown and Spectre. The first, which they dubbed ZombieLoad, uses a similar approach to Meltdown. After preparing tasks in parallel, the processor needs to discard th... » read more

Power/Performance Bits: May 14


Detecting malware with power monitoring Engineers at the University of Texas at Austin and North Carolina State University devised a way to detect malware in large-scale embedded computer systems by monitoring power usage and identifying unusual surges as a warning of potential infection. The method relies on an external piece of hardware that can be plugged into the system to observe and m... » read more

Power/Performance Bits: May 6


Compressing objects Computer scientists at MIT propose a way to improve data compression in memory by focusing on objects rather than cache lines. "The motivation was trying to come up with a new memory hierarchy that could do object-based compression, instead of cache-line compression, because that's how most modern programming languages manage data," said Po-An Tsai, a graduate student at... » read more

Power/Performance Bits: April 30


Printed supercapacitors Researchers at Drexel University and Trinity College created ink for an inkjet printer from MXene, a highly conductive two-dimensional material, which could be used to print flexible energy storage components, such as supercapacitors, in any size or shape. The material shows promise as an ink thanks to its high conductivity and ability to apply easily to surfaces usi... » read more

Power/Performance Bits: April 23


Tiny spectrometer Engineers at the University of Wisconsin-Madison, Sandia National Laboratories, and Huazhong University of Science and Technology developed a miniature spectrometer small enough to integrate with the camera on a typical cellphone without sacrificing accuracy. This miniature sensor is CMOS compatible. "This is a compact, single-shot spectrometer that offers high resolution ... » read more

Power/Performance Bits: April 16


Faster CNN training Researchers at North Carolina State University developed a technique that reduces training time for deep learning networks by more than 60% without sacrificing accuracy. Convolutional neural networks (CNN) divide images into blocks, which are then run through a series of computational filters. In training, this needs to be repeated for the thousands to millions of images... » read more

Power/Performance Bits: April 8


Predicting battery life Researchers at Stanford University, MIT, and Toyota Research Institute developed a machine learning model that can predict how long a lithium-ion battery can be expected to perform. The researchers' model was trained on a few hundred million data points of batteries charging and discharging. The dataset consists of 124 commercial lithium iron phosphate/graphite cells... » read more

Power/Performance Bits: April 2


DNA programming Computer scientists at California Institute of Technology, University of California, Davis, Maynooth University, and Harvard University created a library of DNA molecules that can self-assemble to compute a variety of algorithms. Each molecule represents a six-bit binary number. The library created by the team is made up of around 700 short pieces, or tiles, of DNA. Each DNA... » read more

Power/Performance Bits: Mar. 26


Material holds both electrons, holes Researchers at Ohio State University discovered a material that can hold both electrons and holes. They hope the material, the layered metal crystal NaSn2As2, could simplify electronics, potentially removing the need for multiple layers or materials. "It is this dogma in science, that you have electrons or you have holes, but you don't have both. But our... » read more

Power/Performance Bits: Mar. 19


Explainable AI Researchers from Technische Universität Berlin (TU Berlin), Fraunhofer Heinrich Hertz Institute (HHI), and Singapore University of Technology and Design (SUTD) propose a pair of algorithms to help determine how AI systems reach their conclusions. Explainable AI is an important step towards practical applications, argued Klaus-Robert Müller, Professor for Machine Learning at... » read more

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