203 citations · 677 across the 32 of their papers we have counts for
11 papers · 1 filter
AutoChip: Automating HDL Generation Using LLM Feedback
Shailja Thakur, Jason Blocklove, Hammond Pearce +3
Traditionally, designs are written in Verilog hardware description language (HDL) and debugged by hardware engineers. While this approach is effective, it is time-consuming and err…
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…
Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models
Andrew Taylor, Alexandra Vassar, Jake Renzella +1
In the challenging field of introductory programming, high enrollments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated too…
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…
FLAG: Finding Line Anomalies (in code) with Generative AI
Baleegh Ahmad, Benjamin Tan, Ramesh Karri +1
Code contains security and functional bugs. The process of identifying and localizing them is difficult and relies on human labor. In this work, we present a novel approach (FLAG)…
(Security) Assertions by Large Language Models
Rahul Kande, Hammond Pearce, Benjamin Tan +4
The security of computer systems typically relies on a hardware root of trust. As vulnerabilities in hardware can have severe implications on a system, there is a need for techniqu…
Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
Jason Blocklove, Siddharth Garg, Ramesh Karri +1
Modern hardware design starts with specifications provided in natural language. These are then translated by hardware engineers into appropriate Hardware Description Languages (HDL…
REMaQE: Reverse Engineering Math Equations from Executables
Meet Udeshi, Prashanth Krishnamurthy, Hammond Pearce +2
Cybersecurity attacks on embedded devices for industrial control systems and cyber-physical systems may cause catastrophic physical damage as well as economic loss. This could be a…
Fixing Hardware Security Bugs with Large Language Models
Baleegh Ahmad, Shailja Thakur, Benjamin Tan +2
Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's Codex have demonstrated capabilities in many coding-adjacent domains. In this work we consider how LLMs ma…
A survey of Digital Manufacturing Hardware and Software Trojans
Prithwish Basu Roy, Mudit Bhargava, Chia-Yun Chang +4
Digital Manufacturing (DM) refers to the on-going adoption of smarter, more agile manufacturing processes and cyber-physical systems. This includes modern techniques and technologi…