Tradeoffs Between Edge Vs. Cloud


Increasing amounts of processing are being done on the edge, but how the balance will change between what's computed in the cloud versus the edge remains unclear. The answer may depend as much on the value of data and other commercial reasons as on technical limitations. The pendulum has been swinging between doing all processing in the cloud to doing increasing amounts of processing at the ... » read more

Edge Computing: New Support For Digital Twins


Digital twins are one of the most exciting technology developments to emerge over the past few years. By creating a virtual model of a physical product, then simulating its real-time operation, companies are optimizing maintenance, predicting critical maintenance events and fueling innovation via actual performance feedback. Because simulation requires computational resources and the associated... » read more

Why TinyML Is Such A Big Deal


While machine-learning (ML) development activity most visibly focuses on high-power solutions in the cloud or medium-powered solutions at the edge, there is another collection of activity aimed at implementing machine learning on severely resource-constrained systems. Known as TinyML, it’s both a concept and an organization — and it has acquired significant momentum over the last year or... » read more

New Power, Performance Options At The Edge


Increasing compute intelligence at the edge is forcing chip architects to rethink how computing gets partitioned and prioritized, and what kinds of processing elements and memory configurations work best for a particular application. Sending raw data to the cloud for processing is both time- and resource-intensive, and it's often unnecessary because most of the data collected by a growing nu... » read more

Sensor Fusion Everywhere


How do you distinguish between background noise and the sound of an intruder breaking glass? David Jones, head of marketing and business development for intuitive sensing solutions at Infineon, looks at what types of sensors are being developed, what happens when different sensors are combined, what those sensors are being used for today, and what they will be used for in the future. » read more

Safe And Robust Machine Learning


Deploying machine learning in the real world is a lot different than developing and testing it in a lab. Quenton Hall, AI systems architect at Xilinx, examines security implications on both the inferencing and training side, the potential for disruptions to accuracy, and how accessible these models and algorithms will be when they are used at the edge and in the cloud. This involves everything ... » read more

Getting Realistic About AI


By Olaf Enge-Rosenblatt and Andy Heinig The topic of artificial intelligence (AI) is omnipresent today, both in the news and on popular science shows. The number of possibilities for AI methods to assist people in making decisions are expanding rapidly. There are three main reasons for this: The development of new AI methods (deep learning, reinforcement learning); The continuous ... » read more

For The Edge, It’s All About Location, Location, Location


They are centrally located, are connected to power grids and water systems, and are rapidly thinning out. And you can probably get a new cell phone case or a corn dog in the atrium. Could shopping malls become a future home for the edge? Edge computing has transformed over the last few years from being a vaguely defined concept to a fundamental part of the future data infrastructure. Band... » read more

Automotive Safety Island


The promise of autonomous vehicles is driving profound changes in the design and testing of automotive semiconductor parts. Automotive ICs, once deployed for simple functions like controlling windows, are now performing complex functions related to advanced driver-assist systems (ADAS) and autonomous driving applications. The processing power required results in very large and complex ICs that ... » read more

Shifting Toward Data-Driven Chip Architectures


An explosion in data is forcing chipmakers to rethink where to process data, which are the best types of processors and memories for different types of data, and how to structure, partition and prioritize the movement of raw and processed data. New chips from systems companies such as Google, Facebook, Alibaba, and IBM all incorporate this approach. So do those developed by vendors like Appl... » read more

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