Siddharth Garg
Professor
United StatesResearch Interests
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Positions (1)
Gautam Kamath
New York University
10 months ago
Professor Gautam Kamath is recruiting PhD students to join the Computer Science Department at the Courant Institute of Mathematical Sciences, New York University, starting Fall 2026. The research focus is on learning theory, algorithmic statistics, and trustworthy machine learning, with specific interests in differential privacy, robustness, machine unlearning, data attribution, and AI safety. NYU Computer Science is recognized as a leader in these areas, offering a vibrant research environment and collaboration opportunities with a distinguished group of faculty, including Ainesh Bakshi, Emily Black, Joan Bruna, Eunsol Choi, Siddharth Garg, Yanjun Han, He He, Chinmay Hegde, Pavel Izmailov, Arthur Jacot, Julia Kempe, Qi Lei, Allen Liu, Christopher Musco, Juan Carlos Perdomo, Julia Stoyanovich, Matus Telgarsky, and Andrew Gordon Wilson. All full-time PhD students receive comprehensive financial support, which includes a nine-month stipend, full tuition and fee coverage, and health insurance. Additional compensation is available for those who secure external fellowships or serve as instructional assistants. The department encourages applications from students interested in research abroad at NYU's global campuses in Abu Dhabi or Shanghai. Applicants should have outstanding academic records and a strong command of written English. Required application materials include a statement of purpose, three letters of recommendation, CV, academic transcripts, and proof of English proficiency (TOEFL/IELTS) if applicable. GRE scores are optional. The application deadline is December 12, 2025. Prospective students should mention relevant faculty in their application and can find further details and the application portal at the NYU Computer Science PhD admissions website. For questions, contact Professor Kamath at [email protected].
10 months ago
Articles (1)
In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by automatically completing partial Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks. We evaluate the functional correctness of the generated Verilog code using a specially designed test suite, featuring a custom problem set and testing benches. Here, our fine-tuned open-source CodeGen-16B model outperforms the commercial state-of-the-art GPT-3.5-turbo model with a 1.1% overall increase. Upon testing with a more diverse and complex problem set, we find that the fine-tuned model shows competitive performance against state-of-the-art gpt-3.5-turbo, excelling in certain scenarios. Notably, it demonstrates a 41% improvement in generating syntactically correct Verilog code across various problem categories compared to its pre-trained counterpart, highlighting the potential of smaller, in-house LLMs in hardware design automation. We release our training/evaluation scripts and LLM checkpoints as open-source contributions.
Year:
2024
Collaborators (1)
Benjamin Tan
Assistant Professor
University of Calgary
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