A comprehensive survey on electronic design automation and graph neural networks: Theory and applications
Driven by Moore's law, the chip design complexity is steadily increasing. Electronic Design
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Automation (EDA) has been able to cope with the challenging very large-scale integration …
Chateda: A large language model powered autonomous agent for eda
The integration of a complex set of Electronic Design Automation (EDA) tools to enhance
interoperability is a critical concern for circuit designers. Recent advancements in large …
interoperability is a critical concern for circuit designers. Recent advancements in large …
Compiler Technologies in Deep Learning Co-Design: A Survey
With the rapid development of deep learning applications, general-purpose processors no
longer suffice for deep learning workloads because of the dying of Moore's Law. Thus …
longer suffice for deep learning workloads because of the dying of Moore's Law. Thus …
Lithobench: Benchmarking ai computational lithography for semiconductor manufacturing
Computational lithography provides algorithmic and mathematical support for resolution
enhancement in optical lithography, which is the critical step in semiconductor …
enhancement in optical lithography, which is the critical step in semiconductor …
DevelSet: Deep neural level set for instant mask optimization
As one of the key techniques for resolution enhancement technologies (RETs), optical
proximity correction (OPC) suffers from prohibitive computational costs as feature sizes …
proximity correction (OPC) suffers from prohibitive computational costs as feature sizes …
Aurora: Automated refinement of coarse-grained reconfigurable accelerators
Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units
interconnected through a network-on-chip (NoC), provide higher flexibility than domain …
interconnected through a network-on-chip (NoC), provide higher flexibility than domain …
Generalizable cross-graph embedding for gnn-based congestion prediction
Presently with technology node scaling, an accurate prediction model at early design stages
can significantly reduce the design cycle. Especially during logic synthesis, predicting cell …
can significantly reduce the design cycle. Especially during logic synthesis, predicting cell …
Preroutgnn for timing prediction with order preserving partition: Global circuit pre-training, local delay learning and attentional cell modeling
Pre-routing timing prediction has been recently studied for evaluating the quality of a
candidate cell placement in chip design. It involves directly estimating the timing metrics for …
candidate cell placement in chip design. It involves directly estimating the timing metrics for …
Advancing placement
AB Kahng - Proceedings of the 2021 International Symposium on …, 2021 - dl.acm.org
Placement is central to IC physical design: it determines spatial embedding, and hence
parasitics and performance. From coarse-to fine-grain, placement is conjointly optimized …
parasitics and performance. From coarse-to fine-grain, placement is conjointly optimized …
Openabc-d: A large-scale dataset for machine learning guided integrated circuit synthesis
Logic synthesis is a challenging and widely-researched combinatorial optimization problem
during integrated circuit (IC) design. It transforms a high-level description of hardware in a …
during integrated circuit (IC) design. It transforms a high-level description of hardware in a …