How To Measure ML Model Accuracy

By Bryon Moyer - 01 Apr, 2021 - Comments: 0

Machine learning (ML) is about making predictions about new data based on old data. The quality of any machine-learning algorithm is ultimately determined by the quality of those predictions. However, there is no one universal way to measure that quality across all ML applications, and that has broad implications for the value and usefulness of machine learning. “Every industry, every d... » read more

The Other Side Of AI System Reliability

By Ed Sperling - 09 Mar, 2021 - Comments: 0

Adding intelligence into pervasive electronics will have consequences, but not necessarily what most people expect. Nearly everything electronic these days has some sort of "smart" functionality built in or added on. This can be as simple as a smoke alarm that alerts you when the batteries are running low, a home assistant that learns your schedule and dials the thermostat up or down, or a r... » read more

Neural Networks Without Matrix Math

By Katherine Derbyshire - 17 Sep, 2020 - Comments: 0

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

A Historical Case For Precision

By Lee Vick - 10 Sep, 2020 - Comments: 0

We take for granted today the staggering precision of modern technology. Cars, electronics, robots, and medical equipment all come off the factory floor composed of effortlessly interchangeable parts, but this was not always the case. In the late 18th century most things that required any kind of precision were made by hand, one notable example being the flintlock musket. You see, back then if ... » read more

New Architectures, Much Faster Chips

By Ed Sperling and Ann Mutschler - 31 Aug, 2020 - Comments: 0

The chip industry is making progress in multiple physical dimensions and with multiple architectural approaches, setting the stage for huge performance increases based on more modular and heterogeneous designs, new advanced packaging options, and continued scaling of digital logic for at least a couple more process nodes. A number of these changes have been discussed in recent conferences. I... » read more

Rethinking Architectures Based On Power

By Ed Sperling - 13 Jul, 2020 - Comments: 0

The newest chips being developed for everything from the cloud to the edge of the network look nothing like designs of even a year or two ago. They are architected for speed, from the throughput of high-speed buses and external interconnects to the customized accelerators and arrays of redundant MACs. But many of these designs have barely scratched the surface for saving power, which will becom... » read more

Challenges For Compute-In-Memory Accelerators

By Katherine Derbyshire - 18 Jun, 2020 - Comments: 1

A compute-in-memory (CIM) accelerator does not simply replace conventional logic. It's a lot more complicated than that. Regardless of the memory technology, the accelerator redefines the latency and energy consumption characteristics of the system as a whole. When the accelerator is built from noisy, low-precision computational elements, the situation becomes even more complex. Tzu-Hsian... » read more

How Much Power Will AI Chips Use?

By Ed Sperling - 12 Mar, 2020 - Comments: 1

AI and machine learning have voracious appetites when it comes to power. On the training side, they will fully utilize every available processing element in a highly parallelized array of processors and accelerators. And on the inferencing side they, will continue to optimize algorithms to maximize performance for whatever task a system is designed to do. But as with cars, mileage varies gre... » read more

Using Machine Learning To Break Down Silos

By Ed Sperling - 13 Nov, 2019 - Comments: 0

Jeff David, vice president of AI solutions at PDF Solutions, talks with Semiconductor Engineering about where machine learning can be applied into semiconductor manufacturing, how it can be used to break down silos around different process steps, how active learning works with human input to tune algorithms, and why it’s important to be able to choose different different algorithms for differ... » read more

Where Is The Edge?

By Ed Sperling - 02 Oct, 2019 - Comments: 0

Mike Fitton, senior director of strategic planning at Achronix, talks about what the edge will look like, how that fits in with the cloud, what the requirements are both for processing and for storage, and how this concept will evolve. Edge Knowledge Center Top stories, videos, blogs, white papers all related to the Edge » read more

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Knowledge Centers
Entities, people and technologies explored

Shift Left Is The Tip Of The Iceberg

A transformative change is underway for semiconductor design and EDA. New languages, models, and abstractions will need to be created.

by Brian Bailey

Partitioning In The Chiplet Era

Understanding how chiplets interact under different workloads is critical to ensuring signal integrity and optimal performance in heterogeneous designs.

by Ann Mutschler

3.5D: The Great Compromise

Pros and cons of a middle-ground chiplet assembly that combines 2.5D and 3D-IC.

by Ed Sperling

AI’s Role In Chip Design Widens, Drawing In New Startups

Focus is on letting engineers do much more with the same or fewer resources — and less drudgery.

by Karen Heyman

Key Technologies To Extend EUV To 14 Angstroms

Alongside high-NA EUV will be better-performing photoresists, reduced roughness using passivation and etch, and lateral etching to reduce tip-to-tip dimensions.

by Laura Peters

Memory Fundamentals For Engineers

eBook: Nearly everything you need to know about memory, including detailed explanations of the different types of memory; how and where these are used today; what's changing, which memories are successful and which ones might be in the future; and the limitations of each memory type.

by The SE Staff

What Comes After HBM For Chiplets

The standard for high-bandwidth memory limits design freedom at many levels, but that is required for interoperability. What freedoms can be taken from other functions to make chiplets possible?