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DTSTAMP:20240626T180033Z
LOCATION:Level 2 Exhibit Hall
DTSTART;TZID=America/Los_Angeles:20240625T170000
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UID:dac_DAC 2024_sess233_ETPOST194@linklings.com
SUMMARY:A Data-Driven Automation Method of Liberty Model Characterization 
 for Custom Cells
DESCRIPTION:Engineering Track Poster\n\nDongsub Yoon, Youngjin Ju, and Hyo
 jin Choi (Samsung)\n\nVarious custom cells are used in DRAM and NAND Flash
  memories to optimize power, performance, and area. Liberty model characte
 rization of the custom cells becomes a time-consuming manual task when an 
 automation tool is unable to extract the timing arc and Spice input decks,
  called configuration for characterization in this paper, from them. The c
 onventional approach is to enhance the tool's capabilities so that it can 
 accommodate custom cells which were not previously taken into consideratio
 n. However, as the majority of cell types are remained unchanged across va
 rious projects, the configurations can be reused once manually crafted and
  verified. This study presented a data-driven approach that automates the 
 Liberty model characterization process by mapping a cell to its correspond
 ing configuration with a neural network. We employed graph neural networks
  (GNNs) to establish relationships between cell topologies and the configu
 rations. We implemented supervised classifiers based on widely used GNNs s
 uch as GCN, GraphSAGE, GAT, and GIN, and compare the classification accura
 cies and the numbers of parameters. With GNNs, our method reached over 94%
  accuracy, while the traditional rule-based methods using naming conventio
 n or ad-hoc connectivity rule scored below 75%.\n\nTopic: Back-End Design,
  Embedded Systems, Front-End Design, IP
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