wirelength
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
How Can Reinforcement Learning Achieve Expert-level Placement?
Announce Type: replace Abstract: Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily focuses on wirelength optimization, and therefore often fail to achieve expert-quality layouts. We identify the reward design as the primary cause for the performance gap with experts, and instead of formalizing intricate processes, we circumvent this by directly learning from expert layouts to derive a reward...
Modeling, Optimizing and Exploring Multi-Die FPGA Routing Architectures
Announce Type: new Abstract: Die stacking has enabled 2.5D FPGAs by integrating multiple active dice on a passive silicon interposer for improved yield and capacity, and paved the way for 3D architectures that stack active dice directly atop one another. In these multi-die devices, the unique electrical and physical characteristics of the underlying die-stacking technology impose limitations on inter-die connection density and latency, necessitating a bespoke inter-die routing architecture....
Physics-Guided Geometric Diffusion for Macro Placement Generation
arXiv:2605.16451v2 Announce Type: replace Abstract: Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework.
Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution
Announce Type: new Abstract: Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems. Despite recent advancements in machine learning for spatial coordinate determination, the temporal dimension of placement sequencing remains largely governed by static heuristics. In this work, we demonstrate that the placement sequence is not merely a preprocessing step but a...