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Assessing Economic Viability: A Comparative Analysis of Total Cost of Ownership for Domain-Adapted Large Language Models versus State-of-the-art Counterparts in Chip Design Coding Assistance
This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to coding assistance for chip design.
ChipNeMo: Domain-Adapted LLMs for Chip Design
ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design.
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching
Graph neural networks (GNN) analysis engines are vital for real-world problems that use large graph models.
A general approach for identifying hierarchical symmetry constraints for analog circuit layout
Analog layout synthesis requires some elements in the circuit netlist to be matched and placed symmetrically.
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