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Patrick Yubeaton

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3 papers

1 author row

NeurIPS Conference 2025 Conference Paper

VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification

Patrick Yubeaton
Andre Nakkab
Weihua Xiao
Luca Collini
Ramesh Karri
Chinmay Hegde
Siddharth Garg

This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification methods to evaluate the quality and correctness of generated hardware descriptions. Additionally, we present a suite of specialized small-scale models optimized specifically for Verilog generation. Our work addresses the growing need for automated hardware design tools that can produce verifiably correct implementations from high-level specifications, potentially accelerating the hardware development process while maintaining rigorous correctness guarantees.

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TMLR Journal 2024 Journal Article

PriViT: Vision Transformers for Private Inference

Naren Dhyani
Jianqiao Cambridge Mo
Patrick Yubeaton
Minsu Cho
Ameya Joshi
Siddharth Garg
Brandon Reagen
Chinmay Hegde

The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for private inference using secure multi-party computation (MPC) protocols, due to the large number of non-polynomial operations (self-attention, feed-forward rectifiers, layer normalization). We develop PriViT, a gradient-based algorithm to selectively Taylorize nonlinearities in ViTs while maintaining their prediction accuracy. Our algorithm is conceptually very simple, easy to implement, and achieves improved performance over existing MPC-friendly transformer architectures in terms of the latency-accuracy Pareto frontier.

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NeurIPS Conference 2024 Conference Paper

SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification

Benjamin Feuer
Jiawei Xu
Niv Cohen
Patrick Yubeaton
Govind Mittal
Chinmay Hegde

Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devoted towards a large-scale, systematic comparison of various curation methods. In this work, we take steps towards a formal evaluation of data curation strategies and introduce SELECT, the first large-scale benchmark of curation strategies for image classification. In order to generate baseline methods for the SELECT benchmark, we create a new dataset, ImageNet++, which constitutes the largest superset of ImageNet-1K to date. Our dataset extends ImageNet with 5 new training-data shifts, each approximately the size of ImageNet-1K, and each assembled using a distinct curation strategy. We evaluate our data curation baselines in two ways: (i) using each training-data shift to train identical image classification models from scratch (ii) using it to inspect a fixed pretrained self-supervised representation. Our findings show interesting trends, particularly pertaining to recent methods for data curation such as synthetic data generation and lookup based on CLIP embeddings. We show that although these strategies are highly competitive for certain tasks, the curation strategy used to assemble the original ImageNet-1K dataset remains the gold standard. We anticipate that our benchmark can illuminate the path for new methods to further reduce the gap. We release our checkpoints, code, documentation, and a link to our dataset at https: //github. com/jimmyxu123/SELECT.

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