Focus is on cutting costs across the board, and it turns out there is still quite a bit to cut.

If neural networking architectures are simpler than a CPU, why is it so difficult to create them?

FPGAs are proving to be versatile for many applications.

Neuromorphic computing and machine learning dominate the leading edge of chip design this year.

Are cheaper LiDAR devices on the way?

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.

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

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

The mundane aspects of a system can make or break a solution, and interfaces often define what is possible.

This approach can make designs better and less expensive, but it will require a huge amount of work and more sharing of data.

If neural networking architectures are simpler than a CPU, why is it so difficult to create them?

Unique machine learning algorithms, diminished benefits from scaling, and a need for more granularity are creating a boom for accelerators.