System Bits: April 10


Ultrafast laser beam steering for autonomous cars Researchers at Purdue University and Stanford University reported they have found a novel laser light sensing technology that is more robust and less expensive than currently available with a wide range of uses, including a way to guide fully autonomous vehicles. The team said this innovation is orders of magnitude faster than conventional l... » read more

System Bits: March 13


Wiring quantum computers According to MIT researchers, when we talk about “information technology,” we generally mean the technology part, like computers, networks, and software. But they reminded that the information itself, and its behavior in quantum systems, is a central focus for MIT’s interdisciplinary Quantum Engineering Group (QEG) as it seeks to develop quantum computing and oth... » read more

System Bits: Feb. 20


An evolution in electronics Restoring some semblance to those who have lost the sensation of touch has been a driving force behind Stanford University chemical engineer Zhenan Bao’s decades-long quest to create stretchable, electronically-sensitive synthetic materials. [caption id="attachment_24131783" align="aligncenter" width="300"] Zhenan Bao, the K.K. Lee professor of chemical engineer... » read more

Power/Performance Bits: Feb. 20


Wireless TENG Researchers at Clemson University developed a wireless triboelectric nanogenerator, or W-TENG, that can also act as a battery-free remote. The key to triboelectric nanogenerators is using materials that are opposite in their affinity for electrons so they generate a voltage when brought in contact with each other. For the W-TENG, one electrode was constructed of a multipart... » read more

What’s Next In Neuromorphic Computing


To integrate devices into functioning systems, it's necessary to consider what those systems are actually supposed to do. Regardless of the application, [getkc id="305" kc_name="machine learning"] tasks involve a training phase and an inference phase. In the training phase, the system is presented with a large dataset and learns how to "correctly" analyze it. In supervised learning, the data... » read more

System Bits: Feb. 13


Enabling individual manufacturing apps Researchers at the Fraunhofer Institute for Computer Graphics Research IGD focused on Industrie 4.0 recognize that manufacturing is turning toward batch sizes of one and individualized production in what is sometimes referred to as ‘highly customized mass production.’ [caption id="attachment_24131609" align="aligncenter" width="300"] The scanning ... » read more

Chipmakers Look To New Materials


Graphene, the wonder material rediscovered in 2004, and a host of other two-dimensional materials are gaining ground in manufacturing semiconductors as silicon’s usefulness begins to fade. And while there are a number of compounds in use already, such as gallium arsenide, gallium nitride, and silicon carbide, those materials generally are being confined to specific niche applications. Tran... » read more

Pushing DRAM’s Limits


If humans ever do create a genuinely self-aware artificial intelligence, it may well exhibit the frustration of waiting for data arrive. The access bandwidth of DRAM-based computer memory has improved by a factor of 20x over the past two decades. Capacity increased 128x during the same period. But latency improved only 1.3x, according to Kevin Chang, a researcher at Carnegie Mellon Universit... » read more

System Bits: Nov. 21


MIT-Lamborghini to develop electric car Members of the MIT community were recently treated to a glimpse of the future as they passed through the Stata Center courtyard as the Lamborghini Terzo Millenio (Third Millennium) was in view, which is an automobile prototype for the third millennium. [caption id="attachment_429209" align="alignnone" width="300"] Lamborghini is relying on MIT to make i... » read more

System Bits: Nov. 7


Exposing logic errors in deep neural networks In a new approach meant to brings transparency to self-driving cars and other self-taught systems, researchers at Columbia and Lehigh universities have come up with a way to automatically error-check the thousands to millions of neurons in a deep learning neural network. Their tool — DeepXplore — feeds confusing, real-world inputs into the ... » read more

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