tag: edge computing

Enablers And Barriers For Connecting Diverse Data
By Anne Meixner - 09 Nov, 2021 - Comments: 0

More data is being collected at every step of the manufacturing process, raising the possibility of combining data in new ways to solve engineering problems. But this is far from simple, and combining results is not always possible. The semiconductor industry’s thirst for data has created oceans of it from the manufacturing process. In addition, semiconductor designs large and small now ha... » read more

HBM3: Big Impact On Chip Design
By Ann Mutschler - 14 Oct, 2021 - Comments: 0

An insatiable demand for bandwidth in everything from high-performance computing to AI training, gaming, and automotive applications is fueling the development of the next generation of high-bandwidth memory. HBM3 will bring a 2X bump in bandwidth and capacity per stack, as well as some other benefits. What was once considered a "slow and wide" memory technology to reduce signal traffic dela... » read more

The Everything New Syndrome
By Ed Sperling - 12 Oct, 2021 - Comments: 0

Technology is all about the latest features, the fastest processing, with the lowest power. While that sounds great in marketing pitch, any or all of those factors don't necessarily equate to a better product or long-term user satisfaction. There's a reason semiconductor companies are conservative by nature. They want to know that when they spend tens or hundreds of millions of dollars on a ... » read more

Tradeoffs Between Edge Vs. Cloud
By Brian Bailey - 09 Sep, 2021 - Comments: 0

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
By Ansys - 08 Sep, 2021 - Comments: 0

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
By Bryon Moyer - 02 Sep, 2021 - Comments: 1

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
By Heidi Elliott - 21 Jul, 2021 - Comments: 0

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
By Ed Sperling - 19 Jul, 2021 - Comments: 0

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
By Ed Sperling - 15 Jul, 2021 - Comments: 0

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 - 12 Jul, 2021 - Comments: 0

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

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