Week in Review: IoT, Security, Auto


Internet of Things Dialog Semiconductor is shifting its product portfolio away from smartphones following its pending $600 million deal with Apple. The chip company is looking toward connected-health products and video-game consoles for future growth. The connected-health devices, developed in collaboration with pharmaceutical firms, would monitor blood pressure and check glucose levels, accor... » read more

Week in Review: IoT, Security, Auto


Internet of Things McKinsey & Company identified 10 top trends in the Internet of Things. They include: IoT is a business opportunity, not just a tech opportunity; disciplined execution across multiple use cases is the path to value; and IoT is gradually enabling more subscription business models, but consumers are resistant. Louis Columbus of IQMS provides some IoT data points and id... » read more

Power/Performance Bits: Jan. 29


Neural nets struggle with shape Cognitive psychologists at the University of California Los Angeles investigated how deep convolutional neural networks identify objects and found a big difference between the way these networks and humans perceive objects. In the first of a series of experiments, the researchers showed color images of animals and objects that had been altered to have a diffe... » read more

System Bits: Dec. 26


Adding learning to computer vision UCLA’s Samueli School of Engineering and Stanford University are working on advanced computer vision technology, using artificial intelligence to help vision systems learn to identify faces, objects and other things on their own, without training by humans. The research team breaks up images into chunks they call “viewlets,” then they have the computer ... » read more

System Bits: Nov. 13


Deep learning device identifies airborne allergens To identify and measure airborne biological particles, or bioaerosols, that originate from living organisms such as plants or fungi, UCLA researchers have invented a portable device that uses holograms and machine learning. The device is trained to recognize five common allergens — pollen from Bermuda grass, oak, ragweed and spores from t... » read more

Power/Performance Bits: Sept. 11


Non-toxic photoluminescent nanoparticles Researchers from Osaka University developed a way to improve display technologies using non-toxic light-emitting nanoparticles. In trying to replace cadmium and other toxic materials used in quantum dots, scientists have turned to non-toxic nanoparticles that emit light in an efficient manner by creating I–III–VI semiconductors, such as silver in... » read more

Power/Performance Bits: Aug. 21


Physical neural network Engineers at UCLA built a physical artificial neural network capable of identifying objects as light passes through a series of 3D printed polymer layers. Called a "diffractive deep neural network," it uses the light bouncing from the object itself to identify that object, a process that consumes no energy and is faster than traditional computer-based methods of imag... » read more

Power/Performance Bits: July 31


Training optical neural networks Researchers from Stanford University used an optical chip to train an artificial neural network, a step that could lead to faster, more efficient AI tasks. Although optical neural networks have been recently demonstrated, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical... » read more

The Growing Materials Challenge


By Katherine Derbyshire & Ed Sperling Materials have emerged as a growing challenge across the semiconductor supply chain, as chips continue to scale, or as they are utilized in new devices such as sensors for AI or machine learning systems. Engineered materials are no longer optional at advanced nodes. They are now a requirement, and the amount of new material content in chips contin... » read more

System Bits: April 17


Smartphone microscopes transformed into lab-grade devices with deep learning UCLA Samueli School of Engineering researchers have demonstrated that deep learning techniques can discern and enhance microscopic details in photos taken by smartphones in order to improve the resolution and color details of smartphone images so much that they approach the quality of images from laboratory-grade mic... » read more

← Older posts