Enhancing Online Planning on low-power CPU-GPU SoCs via Bloom Filter Based Memory
This work proposes a new design for online planning for intelligent agents modelled as POMDPs. We introduce an online planner enhanced with Bloom filter memory which we implement and evaluate on a low-power CPU+GPU SoC. Using the DPC++ parallel execution model of the most computing-intensive kernel of our Bloom filter implementation, we reduce the overall planning time by 3.5x to 7.5x for three representative benchmarks in the POMDP literature. Our preliminary results promise new opportunities for using POMDP agents on low-power mobile platforms and in real-time use cases.
Speaker(s)
Denisa Constantinescu
Denisa Constantinescu is a Ph.D. student in Mechatronics and a researcher in the Computer Architecture Department at the University of Malaga. She obtained a Master’s degree in Computer Engineering from the University of Malaga in 2017. She was a Research Visitor at the NUCAR Laboratory (Northeastern University, Boston, USA) in 2018. Her research interests are in parallel computing, robotics, intelligent control systems, optimization, and autonomous decision-making.
Rafael Asenjo
Rafael Asenjo is a Professor of Computer Architecture at the University of Malaga. He has been using TBB since 2008 and over the last ten years, he has focused on productively exploiting heterogeneous chips (CPU+GPU+FPGA) leveraging TBB as the orchestrating framework. He has co-authored the latest book on TBB and is member of the SYCL Advisory Panel.
