Machine-learning (ML) technology is radically changing how robots work and dramatically extending their capabilities. The latest crop of ML technologies is still in its infancy, but it looks like we're at the end of the beginning with respect to robots. Much more looms on the horizon. ML is just one aspect of improved robotics. Robotics has demanding computational requirements, and that's being helped by improvements in multicore processing power.
Sight is one of our most vital senses. Biologically inspired machine vision has developed rapidly in the past decade, to the point that artificial systems can'see' in the sense of gaining valuable information from images and videos1,2, although human vision remains much more efficient. Writing in Nature, Mennel et al.3 report a design for a visual system that, rather like the brain, can be trained to classify simple images in nanoseconds. Modern image sensors such as those in digital cameras are based on semiconductor (solid-state) technology and were developed in the early 1970s; they fall into two main types, known as charge-coupled devices and active-pixel sensors4. These sensors can faithfully capture visual information from the environment, but generate a lot of redundant data.
Researchers at TU Wien (Vienna) have developed an ultra-fast image sensor with a built-in neural network; the sensor can be trained to recognize certain objects. They describe their work on ultrafast machine vision in a paper in Nature. Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN). The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption.
The FTC has given the go-ahead to ON Semiconductor's acquisition of Fairchild Semiconductor. As part of the requirements, ON Semiconductor had to divest its planar insulated gate bipolar transistor business, which will be sold to Littelfuse. The deal has been pending since November 2015 and offers 20 per Fairchild share, approximately 2.4 billion. Sidense demonstrated successful operation of its SHF 1T-OTP one-time programmable memory macros on TSMC's 16FF and 16FFC process nodes. For 16nm implementation, Sidense is adding several enhancements to its architecture including low-voltage reads along with a differential read mode and enhanced security features.
Light-emitting diodes illuminate the exterior of a building in Tokyo, Japan, on Wednesday, Feb. 25, 2015. LEDs are built using semiconductors that allow passage of electrons through the material to produce light, requiring less energy than incandescent bulbs that heat a wire filament until it glows. ON Semiconductor Corp ($ON) is scheduled to release earnings before Monday's open (Feb 13, 2017). On Semiconductor hit a record high of $27.75/share in 2000 during the Dot Com bubble and is currently trading near $14/share. The stock is prone to big moves after reporting earnings and can easily gap up if the numbers are strong.