Leading Chip Maker Rolls Out SoC For Automotive Market With NetSpeed Gemini

Proponents tout freedom for computing architectures, but is the semiconductor ecosystem ready for open-source hardware?

There are multiple layers in which machine learning can help with the creation of semiconductors, but getting there is not as simple as for other application areas.

Part 2: Heterogeneity and architectures become focus as scaling benefits shrink; IP availability may be problematic.

Machine learning needs techniques to prevent adversarial use, along with better data protection and management.

Technology begins to twist in different directions and for different markets.

After two decades of experimentation, the semiconductor industry is scrambling to embrace this approach.

Adding more dimensions creates more data, all of which needs to be processed using new architectural approaches.

RTOS in space; NASA’s safety culture; FPGAs and neural nets; AI in nuclear plants.