AI is vying for circuit and embedded-system design jobs, but in 2025, it still requires a seasoned engineer to ride shotgun.

Training AI-driven design systems that generalize across chips is challenging because it requires learning to optimize the placement of all possible chip netlists onto all possible canvases.

Analog still has a well-known place in music for various components as well. The last piece of course, is how do you learn analog circuits when everyone around you lives in the digital realm?

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms.

As product requirements evolve, engineers and designers search for solutions that enable greater integration, miniaturization, and reliability without compromising on quality or design flexibility. 3D ...

Machine learning has been assisting circuit design automation by replacing human experience with artificial intelligence. This paper presents TAG, a new paradigm of learning the circuit representation ...

AI on the bench: Cadence offers machine learning to smooth chip design A form of reinforcement learning helps in the trade-offs between power, performance, and area in chips.

With the advent of quantum computing, deep learning, and the holographic principle, physicists are no longer peering beyond the event horizon they are mapping out the quantum architecture of space ...