10 citations · 23 across the 4 of their papers we have counts for
4 papers
Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS
Matthew DeLorenzo, Animesh Basak Chowdhury, Vasudev Gohil +4
Existing large language models (LLMs) for register transfer level code generation face challenges like compilation failures and suboptimal power, performance, and area (PPA) effici…
Towards the Imagenets of ML4EDA
Animesh Basak Chowdhury, Shailja Thakur, Hammond Pearce +2
Despite the growing interest in ML-guided EDA tools from RTL to GDSII, there are no standard datasets or prototypical learning tasks defined for the EDA problem domain. Experience…
Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT
Akshaj Kumar Veldanda, Fabian Grob, Shailja Thakur +4
Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit applicability across numerous tasks. One domain of interest is their use in algorithmic hiring, specifically…
VeriGen: A Large Language Model for Verilog Code Generation
Shailja Thakur, Baleegh Ahmad, Hammond Pearce +4
In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and mo…