162 citations · 332 across the 14 of their papers we have counts for
5 papers · 1 filter
Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration
Hasan Genc, Seah Kim, Alon Amid +16
DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to app…
A View on Deep Reinforcement Learning in System Optimization
Ameer Haj-Ali, Nesreen K. Ahmed, Ted Willke +3
Many real-world systems problems require reasoning about the long term consequences of actions taken to configure and manage the system. These problems with delayed and often seque…
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…
Keystone: An Open Framework for Architecting TEEs
Dayeol Lee, David Kohlbrenner, Shweta Shinde +2
Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat…
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
Ameer Haj-Ali, Qijing Huang, William Moses +4
The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit…