Foundry forms ASIC subsidiary as it focuses on 14nm/12nm and above.
Growing volume of data and limited improvements in performance create new opportunities for approaches that never got off the ground.
Why auto tech companies are so concerned about interactions with humans.
Company’s push into deep learning opens door to a variety of new architectures, including tiles, advanced packaging and more customized solutions.
Old solutions don’t necessarily work anymore, particularly at advanced nodes and ultra-low voltage.
AI-enabled systems are being designed to process more data locally as device scaling benefits decline.
Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
What AI and deep learning are good for, what they’re not good for, and why accuracy sometimes works against these systems.
What happens when companies are combined? The outcome often isn’t as good as the announcement.
Growing volume of data and limited improvements in performance create new opportunities for approaches that never got off the ground.
As complexity and device sizes rise, so does the need for an on-chip network.
Experts at the Table, part 1: Are we looking to solve the wrong problems? Where does parallelization work best and why.
Synopsys Co-CEO Aart de Geus explains why systemic complexity will be the next big challenge, and what that means for EDA and the chip industry.
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