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20172023
most citedChip-Chat: Challenges and Opportunities in Conversational Hardware Design

203 citations · 998 across the 44 of their papers we have counts for

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Showing 2023Show all

10 papers · 1 filter

cs.PL2023★ 10 cited

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…

eess.SY2023★ 24 cited

Causative Cyberattacks on Online Learning-based Automated Demand Response Systems

Samrat Acharya, Yury Dvorkin, Ramesh Karri

Power utilities are adopting Automated Demand Response (ADR) to replace the costly fuel-fired generators and to preempt congestion during peak electricity demand. Similarly, third-…

cs.CR2023★ 3 cited

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)…

cs.LG2023★ 1 cited

INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search

Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan +2

Logic synthesis is the first and most vital step in chip design. This steps converts a chip specification written in a hardware description language (such as Verilog) into an optim…

cs.CR2023★ 77 cited

(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…

cs.LG2023★ 203 cited

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…

cs.CR2023★ 2 cited

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…

cs.CR2023★ 1 cited

ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning

Animesh Basak Chowdhury, Lilas Alrahis, Luca Collini +5

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where k…

cs.CR2023★ 75 cited

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…

cs.CR2023★ 1 cited

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…