Architecture, Materials And Software


AI, machine learning and autonomous vehicles will require massive improvements in performance, at the same power consumption level (or better), over today's chips. But it's obvious that the usual approach of shrinking features to improve power/performance isn't going to be sufficient. Scaling will certainly help, particularly on the logic side. More transistors are needed to process a huge i... » read more

AI: The Next Big Thing


The next big thing isn't actually a thing. It's a set of finely tuned statistical models. But developing, optimizing and utilizing those models, which collectively fit under the umbrella of artificial intelligence, will require some of the most advanced semiconductors ever developed. The demand for artificial intelligence is almost ubiquitous. As with all "next big things," it is a horizonta... » read more

The Week in Review: IoT


Finance Toronto-based Ecobee, which markets smart thermostats, raised $61 million in its Series C funding, bringing the total funding for the 11-year-old company to $146 million. Energy Impact Partners led the new round and was joined by Amazon’s Alexa Fund, Relay Ventures, and Thomvest. Ecobee counts Nest Labs, the Google subsidiary, as its chief rival. ThoughtWire, also headquartered in... » read more

Analyzing The Losses In Visually Lossless Compression Algorithms


Over the past few years there has been a remarkable progress in the quality of display devices, with 4K displays becoming the norm, and 8K and 10K displays following closely. However, this increase in quality has led to a tremendous increase in the amount of data being transmitted over display links. To meet these demands most display interfaces are now making use of compression. This white pap... » read more

Executive Insight: Aart de Geus


Aart de Geus, chairman and co-CEO of [getentity id="22035" e_name="Synopsys"], sat down with Semiconductor Engineering to discuss machine learning and big data, the race toward autonomous vehicles, systems vs. chips, software vs. hardware, and the future of EDA. What follows are excerpts of that conversation. SE: The whole tech world is buzzing over data and how it gets used in areas such as... » read more

Neural Adaptive Video Streaming with Pensieve (MIT-CSAIL)


Source:  MIT-CSAIL Hongzi Mao, Ravi Netravali, Mohammad Alizadeh For technical paper link, click here  and MIT's news here Machine-learning system for smoother streaming To combat the frustration of video buffering or pixelation, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “Pensieve,” an artificial intelligence system that ... » read more

System enables large speedups — as much as 88-fold — on common parallel-computing algorithms (MIT)


Source: MIT/ CSAIL: Suvinay Subramanian, Mark C. Jeffrey, Maleen Abeydeera, Hyun Ryong Lee, Victor A. Ying, Joel Emer, Daniel Sanchez As is commonly known, the chips in most modern desktop computers have four cores or processing units, which can run different computational tasks in parallel, but that the chips of the future could have dozens or even hundreds of cores, and taking advantage o... » read more

The Darker Side Of Machine Learning


Machine learning can be used for many purposes, but not all of them are good—or intentional. While much of the work underway is focused on the development of machine learning algorithms, how to train these systems and how to make them run faster and do more, there is a darker side to this technology. Some of that involves groups looking at what else machine learning can be used for. So... » read more

Multi-Robot Path Planning For Swarm of Robots that Can Both Fly, Drive (MIT)


Source: MIT/CSAIL.Brandon Araki, John Strang, Sarah Pohorecky, Celine Qiu, Tobias Naegeli, and Daniela R Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) propose that if robots could be programmed to both walk and take flight, it would open up possibilities including machines that could fly into construction areas or disaster zones that aren’t near ... » read more

What’s Next In Neural Networking?


Faster chips, more affordable storage, and open libraries are giving neural network new momentum, and companies are now in the process of figuring out how to optimize it across a variety of markets. The roots of neural networking stretch back to the late 1940s with Claude Shannon’s Information Theory, but until several years ago this technology made relatively slow progress. The rush towar... » read more

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