The Increasingly Uneven Race To 3nm/2nm

By Mark LaPedus and Ed Sperling - 24 May, 2021 - Comments: 2

Several chipmakers and fabless design houses are racing against each other to develop processes and chips at the next logic nodes in 3nm and 2nm, but putting these technologies into mass production is proving both expensive and difficult. It's also beginning to raise questions about just how quickly those new nodes will be needed and why. Migrating to the next nodes does boost performance an... » read more

Machine Learning At The Edge

By Siemens EDA - 12 May, 2021 - Comments: 0

Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much power, and even generic machine learning accelerators can be overbuilt and are not optimal for power. In this paper, learn about creating new power/memory efficient hardware architectures to meet n... » read more

Kria K26 SOM: The Ideal Platform For Vision AI At The Edge

By Xilinx - 12 May, 2021 - Comments: 0

With various advancements in artificial intelligence (AI) and machine learning (ML) algorithms, many high-compute applications are now getting deployed on edge devices. So, there is a need for an efficient hardware that can execute complex algorithms efficiently as well as adapt to rapid enhancements in this technology. Xilinx's Kria K26 SOM is designed to address the requirements of executing ... » read more

Developers Turn To Analog For Neural Nets

By Bryon Moyer - 06 May, 2021 - Comments: 1

Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Securing AI/ML With A Hardware Root Of Trust

By Thierry Kouthon - 06 May, 2021 - Comments: 0

AI/ML (Artificial Intelligence/Machine Learning) is now pervasive across all industries. It contributes to rationalizing and harnessing the enormous amount of information made available by the current massive wave of digitization. Digitization is transforming how business is run and how value is produced using digital technologies. Data, the raw material of AI/ML and deep learning algorithms, i... » read more

Steep Spike For Chip Complexity And Unknowns

By Ed Sperling - 05 May, 2021 - Comments: 0

Cramming more and different kinds of processors and memories onto a die or into a package is causing the number of unknowns and the complexity of those designs to skyrocket. There are good reasons for combining all of these different devices into an SoC or advanced package. They increase functionality and can offer big improvements in performance and power that are no longer available just b... » read more

Roadblocks For ML in EDA

By Brian Bailey - 28 Apr, 2021 - Comments: 1

Is EDA a suitable space for utilizing machine learning (ML)? The answer depends on a number of factors, including where exactly it is being applied, how much support there is from the industry, and whether there are demonstrable advantages. Exactly where ML will play a role has yet to be decided. Replacing existing heuristics with machine learning, for example, would require an industry-wide... » read more

Manufacturing Bits: April 27

By Mark LaPedus - 27 Apr, 2021 - Comments: 0

Next-gen neuromorphic computing The European Union (EU) has launched a new project to develop next-generation devices for neuromorphic computing systems. The project, called MeM-Scales, plans to develop a novel class of algorithms, devices, and circuits that reproduce multi-timescale processing of biological neural systems. The results will be used to build neuromorphic computing systems th... » read more

Putting Limits On What AI Systems Can Do

By Ed Sperling - 22 Apr, 2021 - Comments: 0

New techniques and approaches are starting to be applied to AI and machine learning to ensure they function within acceptable parameters, only doing what they're supposed to do. Getting AI/ML/DL systems to work has been one of the biggest leaps in technology in recent years, but understanding how to control and optimize them as they adapt isn't nearly as far along. These systems are generall... » read more

Applications, Challenges For Using AI In Fabs

By Mark LaPedus - 14 Apr, 2021 - Comments: 0

Experts at the Table: Semiconductor Engineering sat down to discuss chip scaling, transistors, new architectures, and packaging with Jerry Chen, head of global business development for manufacturing & industrials at Nvidia; David Fried, vice president of computational products at Lam Research; Mark Shirey, vice president of marketing and applications at KLA; and Aki Fujimura, CEO of D2S. Wh... » 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

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?

by Brian Bailey

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

Why Small Fab And Assembly Houses Are Thriving

Megafabs churning out the most advanced processors are not the only game in town.

by Bryon Moyer

tag: machine learning