Things to watch out for in tuning circuits for specific applications.
Using a device’s body bias effect allows designers to tune a circuit’s behavior to meet both power and performance specifications, but getting it right isn’t always easy. Accurate, fast, automated body bias verification is critical to ensure today’s complex designs meet demanding performance, reliability, and power usage specifications.
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Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
Costs of developing a complex chip could run as high as $1.5B, while power/performance benefits are likely to decrease.
Experts at the Table, part 1: Why machine learning works in some cases and not in others.
Continued integration is no longer the natural way forward for semiconductors. What needs to happen to make it easier?
What AI and deep learning are good for, what they’re not good for, and why accuracy sometimes works against these systems.
This more than Moore technology is still ramping up, and problems need to be solved, but it could lead to some fundamental changes.
Cost of porting tools and IP will limit choices at partial nodes and create confusion at others.
The race is on to simulate thermal and electromagnetic effects.
Continued integration is no longer the natural way forward for semiconductors. What needs to happen to make it easier?
The next-gen wireless technology is riddled with problems, but that hasn’t slowed the pace of development.
Electric cars, mobile devices and industrial applications are focusing renewed attention on this technology.
Definitions vary by market and by vendor, but an explosion of data requires more processing to be done locally.
In-chip monitoring techniques are growing for automotive, industrial, and data center applications.
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