The shift from software-defined to AI-defined vehicles raises questions about whether the hardware can keep up.

As models evolve faster than silicon cycles, chip architects must balance flexible compute, data movement, and ...

On-device AI can deliver faster insights, greater autonomy, and less cloud traffic — but only with the right infrastructure.

Researchers at Purdue University and the UCLA published a technical paper titled “Experimental Evidence for the Impact of ...

This eBook offers insights into what’s involved in photonics design, what’s changing (or at least what we know so far), and how those changes will affect semiconductor and electronic design in the ...

Moores Lab AI is betting that chip design AI will only work if it is built around deep semiconductor expertise, not generic ...

Quantum computing applications; monitoring shared memory; simulating solid state batteries; trusting ML in automotive; ...

Verification data is not enough without verification context Most verification environments are good at producing outputs. They generate logs, waveforms, assertions, coverage metrics, pass/fail status ...

A mixture of expert agentic AI systems can focus on their tasks with or without a commanding general, but challenges remain ...

Larger packages, finer routing, and embedded functions are pushing advanced substrates toward application-specific designs.

Aggressive prediction of $1T by 2030 was $700B too low. Here’s why.