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

Deep Learning Spreads

By Ed Sperling - 31 Jan, 2018 - Comments: 0

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for safety-critical devices, such as assisted or autonomous vehicles, as well as for natural language processing where a machine can recognize the intent of words based upon the context of a convers... » read more

New Materials For Computing

By Ed Sperling - 12 Oct, 2017 - Comments: 0

The U.S. Department of Energy rolled out a new program to develop materials for "extreme conditions" for high-performance computing, setting the stage for much more mobile versions of AI and machine learning. This effort, if successful, has interesting implications on a number of levels. For one, the DOE's mandate includes everything from energy security to weaponry, and high-performance com... » read more

Using CNNs To Speed Up Systems

By Ann Mutschler - 10 Aug, 2017 - Comments: 0

Convolutional neural networks (CNNs) are becoming one of the key differentiators in system performance, reversing a decades-old trend that equated speed with processor clock frequencies, the number of transistors, and the instruction set architecture. Even with today's smartphones and PCs, it's difficult for users to differentiate between processors with 6, 8 or 16 cores. But as the amount o... » read more

Speeding Up Neural Networks

By Ed Sperling - 27 Apr, 2017 - Comments: 2

Neural networking is gaining traction as the best way of collecting and moving critical data from the physical world and processing it in the digital world. Now the question is how to speed up this whole process. But it isn't a straightforward engineering challenge. Neural networking itself is in a state of almost constant flux and development, which makes it something of a moving target. Th... » read more

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?

by Brian Bailey

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: recurrent neural networks

There’s More To Machine Learning Than CNNs

Deep Learning Spreads

New Materials For Computing

Using CNNs To Speed Up Systems

Speeding Up Neural Networks