Search Results for author: Hammond Pearce

Found 13 papers, 4 papers with code

Towards the Imagenets of ML4EDA

no code implementations16 Oct 2023 Animesh Basak Chowdhury, Shailja Thakur, Hammond Pearce, Ramesh Karri, Siddharth Garg

Here we describe our experience curating two large-scale, high-quality datasets for Verilog code generation and logic synthesis.

Code Generation Data Augmentation

Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT

no code implementations8 Oct 2023 Akshaj Kumar Veldanda, Fabian Grob, Shailja Thakur, Hammond Pearce, Benjamin Tan, Ramesh Karri, Siddharth Garg

We replicate this experiment on state-of-art LLMs (GPT-3. 5, Bard, Claude and Llama) to evaluate bias (or lack thereof) on gender, race, maternity status, pregnancy status, and political affiliation.

Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models

1 code implementation23 Aug 2023 Andrew Taylor, Alexandra Vassar, Jake Renzella, Hammond Pearce

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 tools that scale to large cohorts.

Language Modelling Large Language Model

VeriGen: A Large Language Model for Verilog Code Generation

no code implementations28 Jul 2023 Shailja Thakur, Baleegh Ahmad, Hammond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri, Siddharth Garg

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 modeling digital systems.

Code Generation Language Modelling +1

LLM-assisted Generation of Hardware Assertions

no code implementations24 Jun 2023 Rahul Kande, Hammond Pearce, Benjamin Tan, Brendan Dolan-Gavitt, Shailja Thakur, Ramesh Karri, Jeyavijayan Rajendran

As vulnerabilities in hardware can have severe implications on a system, there is a need for techniques to support security verification activities.

Code Generation

FLAG: Finding Line Anomalies (in code) with Generative AI

no code implementations22 Jun 2023 Baleegh Ahmad, Benjamin Tan, Ramesh Karri, Hammond Pearce

In this work, we explore the features that help LLMs in this classification and evaluate the performance of FLAG on known bugs.

Chip-Chat: Challenges and Opportunities in Conversational Hardware Design

1 code implementation22 May 2023 Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce

Modern hardware design starts with specifications provided in natural language.

Pop Quiz! Can a Large Language Model Help With Reverse Engineering?

no code implementations2 Feb 2022 Hammond Pearce, Benjamin Tan, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri, Brendan Dolan-Gavitt

Large language models (such as OpenAI's Codex) have demonstrated impressive zero-shot multi-task capabilities in the software domain, including code explanation.

Language Modelling Large Language Model

Examining Zero-Shot Vulnerability Repair with Large Language Models

no code implementations3 Dec 2021 Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, Brendan Dolan-Gavitt

We perform a large scale study of five commercially available, black-box, "off-the-shelf" LLMs, as well as an open-source model and our own locally-trained model, on a mix of synthetic, hand-crafted, and real-world security bug scenarios.

Code Completion

Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions

2 code implementations20 Aug 2021 Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, Ramesh Karri

The most notable of these comes in the form of the first self-described `AI pair programmer', GitHub Copilot, a language model trained over open-source GitHub code.

Code Generation Language Modelling

DAVE: Deriving Automatically Verilog from English

no code implementations27 Aug 2020 Hammond Pearce, Benjamin Tan, Ramesh Karri

While specifications for digital systems are provided in natural language, engineers undertake significant efforts to translate them into the programming languages understood by compilers for digital systems.

Translation

Designing Neural Networks for Real-Time Systems

no code implementations26 Aug 2020 Hammond Pearce, Xin Yang, Partha S. Roop, Marc Katzef, Tórur Biskopstø Strøm

This issue stems largely from the implementation strategies used within common neural network frameworks -- their underlying source code is often simply unsuitable for formal techniques such as static timing analysis.

C++ code

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