Neural Net Computing Explodes

Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors. As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how el... » read more

System Bits: Sept. 13

Big data programming language MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers this week are presenting a new programming language, called Milk, that lets application developers manage memory more efficiently in programs that deal with scattered data points in large data sets. The researchers reminded that in today’s computer chips, memory management is base... » read more

System Bits: Sept. 6

How might AI affect urban life in 2030? In an ongoing project hosted by Stanford University to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines, a panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city. Th... » read more

System Bits: Aug. 30

Probing photon-electron interactions According to Rice University researchers, where light and matter intersect, the world illuminates; where they interact so strongly that they become one, they illuminate a world of new physics. Here, the team is closing in on a way to create a new condensed matter state in which all the electrons in a material act as one by manipulating them with light and a... » read more

Manufacturing Bits: Aug. 16

Safer drinking water Two-dimensional materials are gaining steam in the R&D labs. 2D materials include graphene, boron nitride (BN) and the transition-metal dichalcogenides (TMDs). These materials could one day enable future field-effect transistors (FETs). One TMD, molybdenum disulfide (MoS2), is also generating interest in other fields. Molybdenum disulfide consists of two elements--moly... » read more

System Bits: July 19

Using carbon nanotubes to leapfrog today’s silicon chips According to Stanford University’s Subhasish Mitra, associate professor of electrical engineering and of computer science, and H.-S. Philip Wong, professor of electrical engineering, the future of supercomputing might actually be really, really small. With support from the National Science Foundation, the two are working with IBM and... » read more

System Bits: July 5

Computer vision for automated data collection Stanford University researchers have developed a computer vision system that automates the collection of data about the elements in buildings in order to streamline the remodeling or refurbishment of existing buildings, which can be fraught with delays and cost overruns due to hidden problems. Renovation projects live and die by the quality of i... » read more

Power/Performance Bits: Feb. 2

Single electron transistors A group coordinated by the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) is setting out on a four year program to develop single electron transistors fully compatible with CMOS technology and capable of room temperature operation. The single electron transistor (SET) switches electricity by means of a single electron. The SET is based on a quantum dot (consisting... » read more

System Bits: Dec. 15

Building chips skyscraper style With the aim of boosting electronic performance by factor of a thousand, a team of researchers led by Stanford University engineers have created a skyscraper-like chip design, based on materials more advanced than silicon. For many years, computer systems have been designed with processors and memory chips laid out like single-story structures in a suburb whe... » read more

System Bits: Dec. 1

Extracting the right information in large data sets When solving complex scientific problems, researchers sometimes encounter what is called the curse of dimensionality, that is, they have so much data that they cannot efficiently analyze it. Large data sets can also be expensive and time consuming to acquire, so it is critical to gather only what is necessary. To this end, University of Il... » read more

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