Summary of 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, by Amit Sharma et al.
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
by Amit Sharma, Teodor-Dumitru Ene, Kishor Kunal, Mingjie Liu, Zafar Hasan, Haoxing Ren
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comparative analysis is conducted to evaluate the total cost of ownership (TCO) and performance of large language models (LLMs) in the context of chip design. Specifically, a domain-adapted LLM called ChipNeMo is compared to two leading general-purpose LLMs, Claude 3 Opus and ChatGPT-4 Turbo. The study examines the accuracy, training methodologies, and operational expenditures of these models to determine their efficacy in generating coding assistance for chip design. The results show that domain-adapted LLMs like ChipNeMo demonstrate improved performance at significantly reduced costs compared to general-purpose LLMs. This study aims to provide stakeholders with critical information to select the most economically viable and performance-efficient solutions for their specific needs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are computer programs designed to understand human language. In this paper, researchers compare different types of these models to see which one is best for helping designers create chip designs. They look at three types of models: a special “domain-adapted” model called ChipNeMo and two popular general-purpose models. The study finds that the domain-adapted model is better than the general-purpose models because it can do tasks more accurately while using fewer resources. |
Keywords
* Artificial intelligence * Claude
