machine learning

An increasing number of AI players are building their own silicon and finding that emulation is key to overcoming the major challenges.

Optimizing the way in which machine learning algorithms are implemented in hardware will be a major differentiator for SoCs, especially for edge devices.

Lauro Rizzatti gets a reality check on AI for both design tools and designs themselves from the formal verification specialist.

Artificial intelligence and machine learning require the performance and flexibility offered by embedded FPGA (eFPGA) technology.

Quadrupling the performance of a dedicated CNN engine within an embedded vision processing core brings more complex graph processing within reach.

Our new columnist introduces himself and traces the progress of formal verification over the last two decades. Join the discussion.

This second part looks at Mentor's views on flow neutrality, how DRS360 was born, machine learning and the threat from embedded Trojans.

The equipment giant's Computational Process Control strategy takes a pragmatic approach to Industry 4.0 and is likely to influence EDA tools for incoming nodes.