17 citations · 24 across the 6 of their papers we have counts for
4 papers · 1 filter
A Software-Hardware Co-Optimized Toolkit for Deep Reinforcement Learning on Heterogeneous Platforms
Yuan Meng, Michael Kinsner, Deshanand Singh +2
Deep Reinforcement Learning (DRL) is vital in various AI applications. DRL algorithms comprise diverse compute kernels, which may not be simultaneously optimized using a homogeneou…
Accelerating Deep Neural Network guided MCTS using Adaptive Parallelism
Yuan Meng, Qian Wang, Tianxin Zu +1
Deep Neural Network guided Monte-Carlo Tree Search (DNN-MCTS) is a powerful class of AI algorithms. In DNN-MCTS, a Deep Neural Network model is trained collaboratively with a dynam…
Characterizing Speed Performance of Multi-Agent Reinforcement Learning
Samuel Wiggins, Yuan Meng, Rajgopal Kannan +1
Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing adva…
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
Chen Tang, Kai Ouyang, Zenghao Chai +4
Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using lar…