Power and IR Drop
Cross-source consensus on Power and IR Drop from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Comparisons
Highlighted claims
- Power estimation centers on estimating switching activity at internal nets. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- GRANNITE reports more than an 18.7-fold speedup with less than 5.5% error versus a commercial reference. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- GRANNITE models switching activity using learned directed propagation over fan-in with gate and structural information. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- PGNN aligns IR-drop prediction with the PDN linear system by embedding the residual norm into the loss. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
- IR drop is better served by physics-informed losses than by unconstrained prediction alone. — Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation