no code implementations • 7 Apr 2024 • Siyu Qiu, Benjamin Tan, Hammond Pearce
Training new engineers in digital design is a challenge, particularly when it comes to teaching the complex electronic design automation (EDA) tooling used in this domain.
no code implementations • 16 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.
no code implementations • 8 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.
1 code implementation • 23 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.
no code implementations • 28 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.
no code implementations • 24 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.
no code implementations • 22 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.
1 code implementation • 22 May 2023 • Jason Blocklove, Siddharth Garg, Ramesh Karri, Hammond Pearce
Modern hardware design starts with specifications provided in natural language.
1 code implementation • 13 Dec 2022 • Shailja Thakur, Baleegh Ahmad, Zhenxing Fan, Hammond Pearce, Benjamin Tan, Ramesh Karri, Brendan Dolan-Gavitt, Siddharth Garg
Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors.
no code implementations • 2 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.
no code implementations • 3 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.
2 code implementations • 20 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.
no code implementations • 27 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.
no code implementations • 26 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.