22 citations · 49 across the 6 of their papers we have counts for
4 papers · 1 filter
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Qijing Huang, Ameer Haj-Ali, William Moses +4
The performance of the code a compiler generates depends on the order in which it applies the optimization passes. Choosing a good order--often referred to as the phase-ordering pr…
NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning
Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke +3
One of the key challenges arising when compilers vectorize loops for today's SIMD-compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the co…
Co-Design of Deep Neural Nets and Neural Net Accelerators for Embedded Vision Applications
Kiseok Kwon, Alon Amid, Amir Gholami +3
Deep Learning is arguably the most rapidly evolving research area in recent years. As a result it is not surprising that the design of state-of-the-art deep neural net models proce…
Distributed-Memory Breadth-First Search on Massive Graphs
Aydin Buluc, Scott Beamer, Kamesh Madduri +2
This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchr…