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IIoT Edge Is A Moving Target


Edge computing happens in an industrial IoT (IIoT) system wherever it needs to happen. The business needs for an IIoT system—or one layer of that system—will determine when and where the computing happens. This conclusion, from an introductory report written by the IoT testing organization the Industrial Internet Consortium (IIC), helps explain why no one consistently can say what edge... » read more

Optimizing 5G With AI At The Edge


AI touches our lives in many different ways, and while some AI-enabled applications are highly visible, like the increasingly popular Amazon Echo and Google Home voice-controlled intelligent digital assistants, others are less obvious. But by no means are they less important. For example, AI techniques are essential to the successful rollout of 5G wireless communications. 5G is the develop... » read more

More Processing Everywhere


Simon Segars, CEO of Arm Holdings, sat down with Semiconductor Engineering to discuss security, power, the IoT, a big push at the edge, and the rise of 5G and China. What follows are excerpts of that conversation. SE: Are we making any progress in security? And even if Arm makes progress, does it matter, given there are so many things connected together? Segars: It feels like we’re maki... » read more

Pace Quickens As Machine Learning Moves To The Edge


Artificial intelligence applications are rapidly changing the way society engages with technology. It wasn’t too long ago that your smart phone couldn’t recognize your face or your thumbprint. It also wasn’t too long ago that Alexa wasn’t helping you navigate your day so easily. And not too long ago, odds are, you weren’t developing an application or device that had AI/ML as its ce... » read more

Where The Rubber Hits The Road: Implementing Machine Learning On Silicon


Machine learning (ML) is everywhere these days. The common thread between advanced driver-assistance systems (ADAS) vision applications in our cars and the voice (and now facial) recognition applications in our phones is that ML algorithms are doing the heavy lifting, or more accurately, the inferencing. In fact, neural networks (NN) can even be used in application spaces such as file compressi... » read more

Mobile Machine Learning Hardware At Arm


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

Extending Cloud To The Network Edge


The adoption of multi-gigabit networks and planned roll-out of next generation 5G networks will continue to create greater available network bandwidth as more and more computing and storage services get funneled to the cloud. Increasingly, applications running on IoT and mobile devices connected to the network are becoming more intelligent and compute-intensive. However, with so many resources ... » read more

Designing 5G Chips


5G is the wireless technology of the future, and it’s coming fast. The technology boasts very high-speed data transfer rates, much lower latency than 4G LTE, and the ability to handle significantly higher densities of devices per cell site. In short, it is the best technology for the massive amount of data that will be generated by sensors in cars, IoT devices, and a growing list of next-g... » read more

Navigating The Foggy Edge Of Computing


The National Institute of Standards and Technology (NIST) defines fog computing as a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud or data centers. This model supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated and distributed computing, storage and network connecti... » read more

AI Signals A New Change Of Perspective


A very long time ago, I was a student at MIT, programming with card decks in APL on IBM mainframes and studying AI in a class from Patrick Winston (who took over MIT’s AI lab from the legendary Marvin Minsky). I kept the text book as a reminder of where the world would go. Over four titanic shifts, mainframes/card decks became VAX/VT100, thence to IBM PCs and PC clients tied by Ethernet to co... » read more

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