Summary of Machine Learning-based Feasibility Estimation Of Digital Blocks in Bcd Technology, by Gabriele Faraone et al.
Machine Learning-based feasibility estimation of digital blocks in BCD technology
by Gabriele Faraone, Francesco Daghero, Eugenio Serianni, Dario Licastro, Nicola Di Carolo, Michelangelo Grosso, Giovanna Antonella Franchino, Daniele Jahier Pagliari
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a machine learning-based method for evaluating the feasibility of implementing digital logic on an analog-on-top mixed-signal integrated circuit (IC) design. The approach uses high-level features to predict whether digital blocks can be placed within a reserved area, reducing the need for time-consuming place-and-route trials. This enables rapid feedback between digital and analog back-end designers during top-level placement, streamlining the AMS IC design process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps design better mixed-signal chips by using machines to decide where to put digital parts on the chip. Right now, this is done by hand, which takes a lot of time. The machine learning method uses simple features about the area reserved for digital blocks to figure out if it’s possible to make them work correctly. This makes it faster and easier for designers to get feedback on their ideas. |
Keywords
* Artificial intelligence * Machine learning
