Semiconductor News & Analysis Feed
17 articles
2026-05-27
semiengineering.com
2026-05-27
Semiconductor Engineering
Researchers from Micron Technology and Argonne National Laboratory have released “Understanding Inference Scaling for LLMs: Bottlenecks, Trade-offs, and Performance Principles”.
Abstract
“The transition from standard generative AI to reasoning-centric architectures, exemplified by models capable of extensive Chain-of-Thought (CoT) processing, marks a fundamental paradigm shift in system requireme
2026-05-26
news.google.com
2026-05-26
EE Times
2026-05-26
finance.yahoo.com
2026-05-26
Yahoo Finance
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2026-05-26
news.google.com
2026-05-26
EE Times Asia
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2026-05-24
finance.yahoo.com
2026-05-24
Yahoo Finance
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2026-05-24
www.fool.com
2026-05-24
The Motley Fool
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2026-05-23
finance.yahoo.com
2026-05-23
Yahoo Finance
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2026-05-21
www.fool.com
2026-05-21
The Motley Fool
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2026-05-20
drrobertcastellano.substack.com
2026-05-20
Dr. Robert Castellano's Semiconductor Deep Dive Newsletter
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2026-05-14
ventureburn.com
2026-05-14
Ventureburn
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2026-05-12
www.blocksandfiles.com
2026-05-12
Blocks & Files
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2026-05-11
www.digitimes.com
2026-05-11
digitimes
Micron's senior vice president, Jeremy Werner, told The Circuit Podcast that memory has become a strategic bottleneck for data-center inference, warning that insufficient memory can sharply cut GPU utilization while faster, larger memory can theoretically...The article requires paid subscription.Subscribe Now
2026-05-11
www.digitimes.com
2026-05-11
digitimes
AI-driven demand and new product cycles are accelerating competition among major memory makers, creating pressure across storage and component supply chains. Production is running near full capacity, margins on some DDR5 products have improved, and...The article requires paid subscription.Subscribe Now
2026-05-10
www.tomshardware.com
2026-05-10
Tom's Hardware
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2026-05-07
www.aol.com
2026-05-07
AOL.com
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2026-05-07
www.fool.com
2026-05-07
The Motley Fool
A shift is happening in the artificial intelligence (AI) computing space. Hyperscalers and AI companies are transitioning from training large language models to putting those models to work on real-world data to unlock the productivity gains that this technology promises.A lot ofmoney has been spenton data center infrastructure that's optimized to train models using large datasets. Inference, on t
2026-05-07
www.openpr.com
2026-05-07
openPR.com
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