Design Space Exploration in the Physical-Design of an AI-Processor at 12 nm Using Relative-Placement Methodology
Bibliographic record
Abstract
This paper presents the results of design space exploration (DSE) for the memory-macro placement of a low-power Artificially Intelligent (AI) processor provided by our industry partner in a collaborative project. The commercially available physical design CAD tools for the automatic placement of macros often give unoptimized macro placements that have a large area and total wire length when compared to the manual placement of macros. The goal of this research is to obtain an optimized memory macro placement for the AI processor that provides the lowest possible area, power, wire length, and improved utilization through custom automation to accelerate time. A relative memory macro placement methodology is developed to ease the process of placing the macros. The idea behind the relative placement is that a rectangular object can be placed at 16 different positions around another rectangular object depending on its length and width. Using this methodology for macro placement resulted in a 25% improvement in area requirements, 29% reduction in total power, 40% points improvement in utilization, and a 50% decrease in the wire length when compared with the results for tool placement. Further, the methodology reduced the hand-placement time for memory macros from 2–3 weeks for part of the design to 3–4 days for the complete design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".