Late Breaking Results: Fast System Technology Co-Optimization Framework for Emerging Technology Based on Graph Neural Networks
This paper proposes a fast system technology co-optimization (STCO) framework that optimizes power, performance, and area (PPA) for next-generation IC design, addressing the challenges and opportunities presented by novel materials and device architectures. We focus on accelerating the technology le...
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| creator | Ma, Tianliang Fan, Guangxi Sun, Xuguang Deng, Zhihui Low, Kainlu Shao, Leilai |
| description | This paper proposes a fast system technology co-optimization (STCO) framework
that optimizes power, performance, and area (PPA) for next-generation IC
design, addressing the challenges and opportunities presented by novel
materials and device architectures. We focus on accelerating the technology
level of STCO using AI techniques, by employing graph neural network
(GNN)-based approaches for both TCAD simulation and cell library
characterization, which are interconnected through a unified compact model,
collectively achieving over a 100X speedup over traditional methods. These
advancements enable comprehensive STCO iterations with runtime speedups ranging
from 1.9X to 14.1X and supports both emerging and traditional technologies. |
| doi_str_mv | 10.48550/arxiv.2404.06939 |
| format | Article |
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that optimizes power, performance, and area (PPA) for next-generation IC
design, addressing the challenges and opportunities presented by novel
materials and device architectures. We focus on accelerating the technology
level of STCO using AI techniques, by employing graph neural network
(GNN)-based approaches for both TCAD simulation and cell library
characterization, which are interconnected through a unified compact model,
collectively achieving over a 100X speedup over traditional methods. These
advancements enable comprehensive STCO iterations with runtime speedups ranging
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that optimizes power, performance, and area (PPA) for next-generation IC
design, addressing the challenges and opportunities presented by novel
materials and device architectures. We focus on accelerating the technology
level of STCO using AI techniques, by employing graph neural network
(GNN)-based approaches for both TCAD simulation and cell library
characterization, which are interconnected through a unified compact model,
collectively achieving over a 100X speedup over traditional methods. These
advancements enable comprehensive STCO iterations with runtime speedups ranging
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that optimizes power, performance, and area (PPA) for next-generation IC
design, addressing the challenges and opportunities presented by novel
materials and device architectures. We focus on accelerating the technology
level of STCO using AI techniques, by employing graph neural network
(GNN)-based approaches for both TCAD simulation and cell library
characterization, which are interconnected through a unified compact model,
collectively achieving over a 100X speedup over traditional methods. These
advancements enable comprehensive STCO iterations with runtime speedups ranging
from 1.9X to 14.1X and supports both emerging and traditional technologies.</abstract><doi>10.48550/arxiv.2404.06939</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Emerging Technologies |
| title | Late Breaking Results: Fast System Technology Co-Optimization Framework for Emerging Technology Based on Graph Neural Networks |
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