1 citations · 4 across the 23 of their papers we have counts for
24 papers
Hardware Design and Security in the Era of Chiplets and LLMs
Johann Knechtel, Ozgur Sinanoglu, Paul V. Gratz +1
The semiconductor industry is undergoing a dual revolution: the shift toward heterogeneous 2.5D chiplet systems and the integration of Large Language Models (LLMs) into Electronic…
FinHardBench: Can LLMs Generate Latency-Aware Hardware for Financial Computing?
Weimin Fu, Hejia Zhang, Minghao Shao +6
Can large language models generate not just correct, but fast hardware? This paper investigates the question in financial FPGA design, where 5-10 nanoseconds of latency determines…
CoEvoP&R: Co-Evolving Placement Objectives with Routing Feedback via Large Language Models
Ruogu Chen, Weihua Xiao, Ramesh Karri +1
Analytical placers rely on differentiable objective functions to guide placement, typically combining intermediate surrogate metrics such as half-perimeter wirelength (HPWL) and ce…
LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges
Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri
The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unpr…
Configuration Over Selection: Hyperparameter Sensitivity Exceeds Model Differences in Open-Source LLMs for RTL Generation
Minghao Shao, Zeng Wang, Weimin Fu +5
Benchmarking of open-source LLMs for hardware design focuses on which LLMs to use, while treating inference-time decoding configuration as a secondary concern. This work shows that…
From Natural Language to Silicon: The Representation Bottleneck in LLM Hardware Design
Weimin Fu, Zeng Wang, Minghao Shao +5
Edge applications increasingly demand custom hardware, yet Field-Programmable Gate Array (FPGA) design requires expertise that domain engineers lack. Large Language Models (LLMs) p…
HarmChip: Evaluating Hardware Security Centric LLM Safety via Jailbreak Benchmarking
Zeng Wang, Minghao Shao, Weimin Fu +6
The integration of large language models (LLMs) into electronic design automation (EDA) workflows has introduced powerful capabilities for RTL generation, verification, and design…
VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog
Prithwish Basu Roy, Zeng Wang, Anatolii Chuvashlov +4
Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with common vulnerabilities and we…
LLM4PQC - Accurate and Efficient Synthesis of PQC Cores by Feedback-Driven LLMs
Buddhi Perera, Zeng Wang, Weihua Xiao +4
The design of post-quantum cryptography (PQC) hardware is a complex and hierarchical process with many challenges. A primary bottleneck is the conversion of PQC reference codes fro…
Synthesis-in-the-Loop Evaluation of LLMs for RTL Generation: Quality, Reliability, and Failure Modes
Weimin Fu, Zeng Wang, Minghao Shao +5
RTL generation is more than code synthesis. Designs must be syntactically valid, synthesizable, correct, hardware-efficient. SOTA evaluations stop at functional correctness and do…
TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion
Saideep Sreekumar, Zeng Wang, Akashdeep Saha +6
Hardware Trojans (HTs) remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, stylized benchmarks. We introduce Troja…
TrojanLoC: LLM-based Framework for RTL Trojan Localization
Weihua Xiao, Zeng Wang, Minghao Shao +6
Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the…
LockForge: Automating Paper-to-Code for Logic Locking with Multi-Agent Reasoning LLMs
Akashdeep Saha, Zeng Wang, Prithwish Basu Roy +3
Despite rapid progress in logic locking (LL), reproducibility remains a challenge as codes are rarely made public. We present LockForge, a first-of-its-kind, multi-agent large lang…
NetDeTox: Adversarial and Efficient Evasion of Hardware-Security GNNs via RL-LLM Orchestration
Zeng Wang, Minghao Shao, Akashdeep Saha +4
Graph neural networks (GNNs) have shown promise in hardware security by learning structural motifs from netlist graphs. However, this reliance on motifs makes GNNs vulnerable to ad…
VeriDispatcher: Multi-Model Dispatching through Pre-Inference Difficulty Prediction for RTL Generation Optimization
Zeng Wang, Weihua Xiao, Minghao Shao +4
Large Language Models (LLMs) show strong performance in RTL generation, but different models excel on different tasks because of architecture and training differences. Prior work m…
Large Language Models (LLMs) for Electronic Design Automation (EDA)
Kangwei Xu, Denis Schwachhofer, Jason Blocklove +10
With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involve…
VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code
Raghu Vamshi Hemadri, Jitendra Bhandari, Andre Nakkab +5
Modern chip design is complex, and there is a crucial need for early-stage prediction of key design-quality metrics like timing and routing congestion directly from Verilog code (a…
SALAD: Systematic Assessment of Machine Unlearning on LLM-Aided Hardware Design
Zeng Wang, Minghao Shao, Rupesh Karn +6
Large Language Models (LLMs) offer transformative capabilities for hardware design automation, particularly in Verilog code generation. However, they also pose significant data sec…
Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form
Prithwish Basu Roy, Akashdeep Saha, Manaar Alam +4
Automated Verilog code synthesis poses significant challenges and typically demands expert oversight. Traditional high-level synthesis (HLS) methods often fail to scale for real-wo…
VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination
Zeng Wang, Minghao Shao, Jitendra Bhandari +5
Large Language Models (LLMs) have revolutionized code generation, achieving exceptional results on various established benchmarking frameworks. However, concerns about data contami…
VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding
Zeng Wang, Minghao Shao, Mohammed Nabeel +7
Large language models (LLMs) offer significant potential for coding, yet fine-tuning (FT) with curated data is essential for niche languages like Verilog. Using proprietary intelle…
RTL-Breaker: Assessing the Security of LLMs against Backdoor Attacks on HDL Code Generation
Lakshmi Likhitha Mankali, Jitendra Bhandari, Manaar Alam +4
Large language models (LLMs) have demonstrated remarkable potential with code generation/completion tasks for hardware design. In fact, LLM-based hardware description language (HDL…
ASCENT: Amplifying Power Side-Channel Resilience via Learning & Monte-Carlo Tree Search
Jitendra Bhandari, Animesh Basak Chowdhury, Mohammed Nabeel +4
Power side-channel (PSC) analysis is pivotal for securing cryptographic hardware. Prior art focused on securing gate-level netlists obtained as-is from chip design automation, negl…
Lightweight Countermeasures Against Static Power Side-Channel Attacks
Jitendra Bhandari, Mohammed Nabeel, Likhitha Mankali +3
This paper presents a novel defense strategy against static power side-channel attacks (PSCAs), a critical threat to cryptographic security. Our method is based on (1) carefully tu…