Blog Review: Nov. 7


Arm's Shidhartha Das looks into maximizing the benefits of power delivery networks and explains a non-intrusive technique using an on-chip digital storage oscilloscope that can directly sample the power-rails to probe potential runtime bugs due to power delivery weaknesses. Synopsys' Snigdha Dua argues that scrambling is one of the most important features introduced in HDMI 2.0 and takes a l... » read more

System Bits: Nov. 6


Keeping data private To preserve privacy during data collection from the Internet, Stanford University researchers have developed a new technique that maintains personal privacy given that the many devices part of our daily lives collect information about how we use them. Stanford computer scientists Dan Boneh and Henry Corrigan-Gibbs created the Prio method for keeping collected data priva... » read more

System Bits: Oct. 30


Ethics, regional differences for programming autonomous vehicles MIT researchers have revealed some distinct global preferences concerning the ethics of autonomous vehicles, as well as some regional variations in those preferences based on a recently completed survey. [caption id="attachment_24139620" align="alignleft" width="300"] Ethical questions involving autonomous vehicles are the foc... » read more

System Bits: Oct. 23


Adapting machine learning for use in scientific research To better tailor machine learning for effective use in scientific research, the U.S. Department of Energy has awarded a collaborative grant to a group of researchers, including UC Santa Barbara mathematician Paul Atzberger, to establish a new data science research center. According to UCSB, the Physics-Informed Learning Machines for M... » read more

Adding AI To The IoT


The Internet of Things is about to undergo a radical change, fueled by vast number of things coupled with an almost pervasive presence of AI. The IoT today encompasses a long list of vertical markets, all of them connected to the Internet but not necessarily to each other. The concept of the IoT really began taking off in 2015, when a combination of data analytics, high-speed, affordable and... » read more

Week in Review: IoT, Security, Auto


Deals Dialog Semiconductor made a blockbuster deal with Apple – the chip company will license power management technologies and transfer some assets to Apple, which will use them in their internal chip research and development. More than 300 Dialog employees, mostly engineers, will join Apple, which will pay $300 million in cash for the transaction and prepay another $300 million for Dialog ... » read more

Power/Performance Bits: Oct. 9


Spray-on antenna Engineers at Drexel University developed a sprayable form of the 2D material MXene that can be used to create antennas on nearly any surface. The antennas perform as well or better than the ones currently used in mobile devices and RFID tags. The MXene titanium carbide can be dissolved in water to create an ink or paint. The exceptional conductivity of the material enables ... » read more

Week in Review: IoT, Security, Auto


Internet of Things Amazon Web Services announced that Iridium Communications has joined the AWS Partner Network. AWS and Iridium have collaborated on development of Iridium CloudConnect, a service that enables worldwide coverage for Internet of Things applications through Iridium’s satellite network. AWS IoT is being paired with Iridium IoT services as a result. IHS Markit forecasts there wi... » read more

System Bits: Oct. 2


Computer algorithms exhibit prejudice based on datasets Researchers at Cardiff University and MIT have shown that groups of autonomous machines are capable of demonstrating prejudice by identifying, copying, and learning this behavior from one another. The team noted that while it may seem that prejudice is a human-specific phenomenon that requires human cognition to form an opinion of, or ... » read more

System Bits: Sept. 18


Better AI technique for chemistry predictions CalTech researchers have found a new technique that uses machine learning more effectively to predict how complex chemicals will react to reagents. The tool is a new twist on similar machine learning techniques to find more effective catalysts without having the time-consuming trial-and-error research, making it a time-saver for drug researchers. ... » read more

← Older posts