5 papers
Discovering Sparse Recovery Algorithms Using Neural Architecture Search
Patrick Yubeaton, Sarthak Gupta, M. Salman Asif +1
The design of novel algorithms for solving inverse problems in signal processing is an incredibly difficult, heuristic-driven, and time-consuming task. In this short paper, we the…
VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification
Patrick Yubeaton, Andre Nakkab, Weihua Xiao +4
This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification met…
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference
Patrick Yubeaton, Tareq Mahmoud, Shehab Naga +8
As they become more capable, large language models (LLMs) have continued to rapidly increase in size. This has exacerbated the difficulty in running state of the art LLMs on small,…
TruncFormer: Private LLM Inference Using Only Truncations
Patrick Yubeaton, Jianqiao Cambridge Mo, Karthik Garimella +4
Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains…
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification
Benjamin Feuer, Jiawei Xu, Niv Cohen +3
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 devote…