A structure-aware framework for learning device placements on computation graphs

no code implementations • 23 May 2024 • Shukai Duan, Heng Ping, Nikos Kanakaris, Xiongye Xiao, Peiyu Zhang, Panagiotis Kyriakis, Nesreen K. Ahmed, Guixiang Ma, Mihai Capota, Shahin Nazarian, Theodore L. Willke, Paul Bogdan

To bridge the gap between encoder-placer and grouper-placer techniques, we propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit using reinforcement learning.

graph partitioning Graph Representation Learning +1

Leveraging Reinforcement Learning and Large Language Models for Code Optimization

no code implementations • 9 Dec 2023 • Shukai Duan, Nikos Kanakaris, Xiongye Xiao, Heng Ping, Chenyu Zhou, Nesreen K. Ahmed, Guixiang Ma, Mihai Capota, Theodore L. Willke, Shahin Nazarian, Paul Bogdan

We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.

Language Modelling reinforcement-learning +1