Will Markets For ML Models Materialize?

By Bryon Moyer - 02 Dec, 2021 - Comments: 0

Developers are spending increasing amounts of time and effort in creating machine-learning (ML) models for use in a wide variety of applications. While this will continue as the market matures, at some point some of these efforts might be seen as reinventing models over and over. Will developers of successful models ever have a marketplace in which they can sell those models as IP to other d... » read more

Challenges In Developing A New Inferencing Chip

By Ed Sperling - 01 Jul, 2021 - Comments: 0

Cheng Wang, co-founder and senior vice president of software and engineering at Flex Logix, sat down with Semiconductor Engineering to explain the process of bringing an inferencing accelerator chip to market, from bring-up, programming and partitioning to tradeoffs involving speed and customization. SE: Edge inferencing chips are just starting to come to market. What challenges di... » read more

Are Better Machine Training Approaches Ahead?

By Bryon Moyer - 01 Jul, 2020 - Comments: 1

We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

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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.

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

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

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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

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tag: training models

Will Markets For ML Models Materialize?

Challenges In Developing A New Inferencing Chip

Are Better Machine Training Approaches Ahead?