no code implementations • 27 Nov 2023 • Yu-Chen Lin, Akhilesh Kumar, Norman Chang, Wenliang Zhang, Muhammad Zakir, Rucha Apte, Haiyang He, Chao Wang, Jyh-Shing Roger Jang
We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data renovation techniques to improve the semantic representation of embeddings' space; (ii) introducing the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data renovation reliability; (iii) developing the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique; and (iv) effectively refactoring existing scripts to generate new and high-quality scripts with LLMs.
no code implementations • 10 Sep 2022 • Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration.
no code implementations • 7 Oct 2021 • Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
no code implementations • 29 Sep 2021 • Rishikesh Ranade, Derek Christopher Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.