limbo018/DREAMPlace
Deep learning toolkit-enabled VLSI placement observed · 2026-08-28
Health v2 · maintenance only
74/100
- Activity 93
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2743
- days_rel: n/a
- days_push: 46
- n_releases_24m: 0
Adoption not part of the score
1044 stars · 280 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
DREAMPlace is a GPU-accelerated VLSI placement tool that reformulates placement as a deep-learning-like optimization using PyTorch. It supports global, legalization, and detailed placement with large speedups over CPU-based placers.
Use cases
- place standard cells in a VLSI design
- run GPU-accelerated global placement and legalization
- benchmark placement algorithms on ISPD contest benchmarks
- accelerate detailed placement of million-cell designs
- experiment with deep-learning-based physical design
When to choose
- you need fast placement of large netlists on GPU hardware
- you want a flexible PyTorch-based research platform for placement algorithms
When to avoid
- you need a full commercial P&R flow with routing and signoff
- you have no GPU and very large designs where CPU performance is insufficient
Facets
library · maturity active
machine-learning gpu-computing simulation hardware machine-learning python vlsi placement eda pytorch physical-design linux gpu docker
1 source
- readme: https://github.com/limbo018/DREAMPlace · fetched 2026-08-28 · daad81855617
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| limbo018/DREAMPlace | main | 74 |
For agents
markdown · JSON · MCP: product_card(name="limbo018/DREAMPlace")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem