There’s More To Machine Learning Than CNNs

By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

Leveraging Data In Chipmaking

By Ed Sperling - 12 Nov, 2019 - Comments: 0

John Kibarian, president and CEO of PDF Solutions, sat down with Semiconductor Engineering to talk about the impact of data analytics on everything from yield and reliability to the inner structure of organizations, how the cloud and edge will work together, and where the big threats are in the future. SE: When did you recognize that data would be so critical to hardware design and manufact... » read more

Here Come The Economists

By Ed Sperling - 07 May, 2019 - Comments: 0

Big data is undergoing some big changes. For years, the challenge was getting enough good data to create models for everything from Wall Street trends to traffic routing. But with an influx of data from billions of sensors and electronic transactions, data is no longer in short supply. In fact, there is so much data pouring in that companies need to figure out what to do with it. That requi... » read more

Building AI SoCs

By Ed Sperling - 25 Oct, 2018 - Comments: 0

Ron Lowman, strategic marketing manager at Synopsys, looks at where AI is being used and how to develop chips when the algorithms are in a state of almost constant change. That includes what moves to the edge versus the data center, how algorithms are being compressed, and what techniques are being used to speed up these chips and reduce power. https://youtu.be/d32jtdFwpcE ... » read more

Making AI Run Faster

By Ed Sperling - 11 Oct, 2018 - Comments: 0

The semiconductor industry has woken up to the fact that heterogeneous computing is the way forward and that inferencing will require more than a GPU or a CPU. The numbers being bandied about by the 30 or so companies working on this problem are 100X improvements in performance. But how to get there isn't so simple. It requires four major changes, as well as some other architectural shifts. ... » read more

DOM-Based Cross-Site Scripting

By Synopsys - 10 Oct, 2018 - Comments: 0

DOM-based attacks are a misunderstood, serious, and pervasive source of risk in contemporary web applications. The language that drives the web, JavaScript, is easy to understand and hard to master; junior and senior developers routinely make mistakes. Mix difficulty to master with an enormous attack surface, and you have the perfect storm for widespread vulnerability. These risks expose web ap... » read more

IoT Meets ML

By Ed Sperling - 02 Aug, 2018 - Comments: 0

AI and machine learning are the next big things, and they're going make a huge difference in the adoption and capabilities of the IoT. Unlike previous technology approaches, AI, machine learning and deep learning are based on patterns. In effect, they raise up the level of abstraction for data. An image of a cat can be megabytes of data, and a cat taken from all angles may be gigabytes of da... » read more

An Innovator’s Vision

By Brian Bailey - 28 Aug, 2017 - Comments: 1

I had the pleasure of talking with [getperson id="11764" comment="Lucio Lanza"], managing director of Lanza techVentures, when I was researching my article on design innovation earlier this month. One thing that sets successful business people apart is their ability to see patterns, to correctly identify how those patterns fit together and progress and, based on those, to know which way to evol... » read more

Building Chips That Can Learn

By Ed Sperling - 29 Sep, 2016 - Comments: 0

The idea that devices can learn optimal behavior rather than relying on more generalized hardware and software is driving a resurgence in artificial intelligence, machine leaning, and cognitive computing. But architecting, building and testing these kinds of systems will require broad changes that ultimately could impact the entire semiconductor ecosystem. Many of these changes are wel... » read more

Plugging Holes In Machine Learning

By Ed Sperling - 19 Sep, 2016 - Comments: 0

The number of companies using machine learning is accelerating, but so far there are no tools to validate, verify and debug these systems. That presents a problem for the chipmakers and systems companies that increasingly rely on machine learning to optimize their technology because, at least for now, it creates the potential for errors that are extremely difficult to trace and fix. At the s... » 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?