Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Pearce, Hammond"'
Autor:
Sun, Ruoxi, Chang, Jiamin, Pearce, Hammond, Xiao, Chaowei, Li, Bo, Wu, Qi, Nepal, Surya, Xue, Minhui
Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence, combining diverse data modalities to enhance learning and understanding across a wide range of applications. However, this integration also brings uni
Externí odkaz:
http://arxiv.org/abs/2411.11195
Autor:
Blocklove, Jason, Thakur, Shailja, Tan, Benjamin, Pearce, Hammond, Garg, Siddharth, Karri, Ramesh
Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are emerging as a
Externí odkaz:
http://arxiv.org/abs/2411.11856
Autor:
Mei, Xiang, Singaria, Pulkit Singh, Del Castillo, Jordi, Xi, Haoran, Abdelouahab, Benchikh, Bao, Tiffany, Wang, Ruoyu, Shoshitaishvili, Yan, Doupé, Adam, Pearce, Hammond, Dolan-Gavitt, Brendan
High-quality datasets of real-world vulnerabilities are enormously valuable for downstream research in software security, but existing datasets are typically small, require extensive manual effort to update, and are missing crucial features that such
Externí odkaz:
http://arxiv.org/abs/2408.02153
Large Language Models (LLMs) have demonstrated capabilities for producing code in Hardware Description Languages (HDLs). However, most of the focus remains on their abilities to write functional code, not test code. The hardware design process consis
Externí odkaz:
http://arxiv.org/abs/2405.02326
Autor:
Blocklove, Jason, Raz, Md, Roy, Prithwish Basu, Pearce, Hammond, Krishnamurthy, Prashanth, Khorrami, Farshad, Karri, Ramesh
Cybersecurity threats in Additive Manufacturing (AM) are an increasing concern as AM adoption continues to grow. AM is now being used for parts in the aerospace, transportation, and medical domains. Threat vectors which allow for part compromise are
Externí odkaz:
http://arxiv.org/abs/2404.15446
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. Learners will typically deploy designs in the Verilog and VHDL hardware descri
Externí odkaz:
http://arxiv.org/abs/2404.07235
Autor:
Ullah, Saad, Han, Mingji, Pujar, Saurabh, Pearce, Hammond, Coskun, Ayse, Stringhini, Gianluca
Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We thus develop SecLLMHolmes, a fully automated evaluation framework
Externí odkaz:
http://arxiv.org/abs/2312.12575
Autor:
Thakur, Shailja, Blocklove, Jason, Pearce, Hammond, Tan, Benjamin, Garg, Siddharth, Karri, Ramesh
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 error-prone for complex designs. Large language models (LLMs) are promisi
Externí odkaz:
http://arxiv.org/abs/2311.04887
Publikováno v:
ICCAD 2023
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 from the computer vision community suggests that such datasets are cru
Externí odkaz:
http://arxiv.org/abs/2310.10560
Autor:
Veldanda, Akshaj Kumar, Grob, Fabian, Thakur, Shailja, Pearce, Hammond, Tan, Benjamin, Karri, Ramesh, Garg, Siddharth
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 in matching resumes with job categories. Yet, this introduces issues o
Externí odkaz:
http://arxiv.org/abs/2310.05135