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.


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