machine learning

Processor IP company will incorporate custom instructions and other changes in its superscalar core, which includes a novel memory unit for sparse matrices.

Cadence Design Systems has expanded its use of machine learning for EDA into PCB design, joining a growing number of suppliers who have decided it is a sector that needs the AI treatment.

As first silicon success declines, new software aims to provide a more holistic view of coverage data from multiple sources.

The key to exploiting AI is being clear about where its family of technologies can help to improve and democratize design.

Aside from the keynotes and technical papers, the networking at an event like DVCon Europe provides a way to keep open-source EDA on the road.

Cadence has brought the inputs for its AI-driven tools under the umbrella of a big-data collection platform and added functional verification to the list of products that use machine learning.

Cadence president expects expanded role for reinforcement learning in tool portfolio and looks for help on AI for verification.

CXL is a strongly-backed technology aimed at improving connectivity across datacenters handling high demand HPC and AI applications.

Cadence has used machine-learning techniques originally developed for its Cerebrus tool to build software that can speed up multiphysics analysis.

VLSI Symposium 2022 will show the rapid development taking place in oxide-based replacements for traditional DRAM cells as well as the emerging area of memory-based low-power machine learning.