97 citations · 107 across the 3 of their papers we have counts for
4 papers · 1 filter
An Imitation Learning Approach for Cache Replacement
Evan Zheran Liu, Milad Hashemi, Kevin Swersky +2
Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses. To increase cache hits, we focus on the problem of ca…
Neural Execution Engines: Learning to Execute Subroutines
Yujun Yan, Kevin Swersky, Danai Koutra +2
A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms. Th…
Learning Execution through Neural Code Fusion
Zhan Shi, Kevin Swersky, Daniel Tarlow +2
As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new models that can understand and optimize the execution of general purpose cod…
Learning Memory Access Patterns
Milad Hashemi, Kevin Swersky, Jamie A. Smith +5
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recen…