tag: machine learning

Artificial, With Questionable Intelligence
By Ed Sperling - 05 Apr, 2018 - Comments: 1

A common theme is emerging in the race to develop big machines that can navigate through a world filled with people, animals, and other assorted objects—if an accident is inevitable, what options are available to machines and how should they decide?   This question was raised at a number of semiconductor industry conferences over the past few weeks, which is interesting because this idea h... » read more

The Week in Review: IoT
By Jeff Dorsch - 30 Mar, 2018 - Comments: 0

Regulation The Consumer Product Safety Commission is accepting public comments on “potential safety issues and hazards associated with Internet-connected consumer products.” The agency is concerned about “unexpected operating conditions” with Internet of Things devices, along with hacking that could start fires through a stovetop or grill, and the potential compromising of home safety ... » read more

When AI Goes Awry
By Ed Sperling - 26 Mar, 2018 - Comments: 0

The race is on to develop intelligent systems that can drive cars, diagnose and treat complex medical conditions, and even train other machines. The problem is that no one is quite sure how to diagnose latent or less-obvious flaws in these systems—or better yet, to prevent them from occurring in the first place. While machines can do some things very well, it's still up to humans to devise... » read more

Applying Machine Learning To Chips
By Ann Mutschler - 22 Mar, 2018 - Comments: 0

The race is on to figure out how to apply analytics, data mining and machine learning across a wide swath of market segments and applications, and nowhere is this more evident than in semiconductor design and manufacturing. The key with ML/DL/AI is understanding how devices react to real events and stimuli, and how future devices can be optimized. That requires sifting through an expandi... » read more

System Bits: March 20
By Ann Mutschler - 20 Mar, 2018 - Comments: 0

Design has consequences Carnegie Mellon University design students are exploring ways to enhance interactions with new technologies and the power of artificial intelligence. Assistant Professor Dan Lockton teaches the course, "Environments Studio IV: Designing Environments for Social Systems" in CMU's School of Design and leads the school's new Imaginaries Lab. “We want the designers of ... » read more

AI: The Next Big Thing
By Ed Sperling - 15 Mar, 2018 - Comments: 2

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

Intelligence At The Edge Is Transforming Our World
By Ian Forsyth - 08 Mar, 2018 - Comments: 0

Innovation comes in all forms in technology, from software and hardware to displays all the way to the human-machine interface. As devices and systems become more intelligent, the onus on humans to learn the machine’s ways is shifting. Until now, interaction with smart devices has largely relied on our ability to manipulate the machines; to learn their language to input and extract the inform... » read more

Mobile Machine Learning At Arm
By Arm - 07 Mar, 2018 - Comments: 0

Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially neural networks, to improve compute efficiency. However, machine learning is typically just one processing stage in complex end-to-end applications, which invol... » read more

Is Advanced Packaging The Next SoC?
By Ed Sperling - 05 Mar, 2018 - Comments: 1

Device scaling appears to be possible down to 1.2nm, and maybe even beyond that. What isn't obvious is when scaling will reach that node, how many companies will actually use it, or even what chips will look like when foundries actually start turning out these devices using multi-patterning with high-NA EUV and dielectrics with single-digit numbers of atoms. There are two big changes playing... » read more

Low-Power Deep Learning Implementation For Automotive ICs
By Gordon Cooper - 01 Mar, 2018 - Comments: 0

Examples of automotive applications abound where high-performance, low-power embedded vision processors are used, from in-car driver drowsiness detection, to a self-driving car ‘seeing’ the road ahead with pedestrians, oncoming cars, or the occasional animal crossing the road. Implementing deep learning in these types of applications requires a lot of processing power with the lowest possib... » read more

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