Autonomous machines promise to transform industries, but memory bottlenecks, power constraints, specialized architectures, ...
SE: AI is disruptive to EDA, the semiconductor industry, and to their customers. One early indication is that computer ...
Shared resources, a mix of tools running at different speeds, and multiple dies are adding new challenges for design teams.
How data center operators can protect critical data and communications without compromising the performance demanded by distributed AI and HPC workloads.
Who verifies the verifier? This is the age-old question for anyone involved in the verification of semiconductor systems. Who ...
Chiplets for physical AI; rack-level considerations; verification complexity; sparse matrix workloads; AI data centers.
Technology is only useful if it improves an existing methodology. Property generation is still refining that goal, but in the ...
In an era dominated by machine learning, the von Neumann architecture is struggling to stay relevant. The world has changed from being control-centric to one that is data-centric, pushing processor ...
Meter-scale quantum interconnects; 2.5D/3D GPU integration and embodied emissions; secure memory-dependent prefetching; routing-aware analog placement; AI-enhanced flip-chip defect detection; ...
Physics-based analysis becomes more consequential as chips, packages, boards, and systems are designed together.
Researchers at Chinese Academy of Sciences, Jilin University, State Key Laboratory of Advanced Manufacturing for Optical ...
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