34 citations · 41 across the 4 of their papers we have counts for
4 papers
The Next 700 ML-Enabled Compiler Optimizations
S. VenkataKeerthy, Siddharth Jain, Umesh Kalvakuntla +6
There is a growing interest in enhancing compiler optimizations with ML models, yet interactions between compilers and ML frameworks remain challenging. Some optimizations require…
Kepler: Robust Learning for Faster Parametric Query Optimization
Lyric Doshi, Vincent Zhuang, Gaurav Jain +5
Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance.…
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon +6
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine learning (ML) accelerators while delivering performance and energy efficiency on par with a monolithic l…
Differentiable Architecture Search for Reinforcement Learning
Yingjie Miao, Xingyou Song, John D. Co-Reyes +4
In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL? Using the original DARTS as…