Presentation
Invited: Algorithm and Hardware Co-Design for Energy-Efficient Neural SLAM
DescriptionSimultaneous Localization and Mapping (SLAM) is an important but costly computing workload in practical mobile robot applications. Recently, neural network-based SLAM has attracted a lot of attention because of its strong task performance enabled by the powerful learning capability of neural networks. On the other hand, it further increases the complexity of the SLAM module. In this work, we propose to perform algorithm and hardware co-design towards accelerating neural SLAM. By jointly optimizing the SLAM model and the micro-architecture dataflow, our approach significantly improves the computation and energy efficiency while preserving high task performance. Experiments across different scenarios demonstrate the efficacy of this co-optimization approach.
Event Type
Special Session (Research)
TimeTuesday, June 253:30pm - 4:00pm PDT
Location3006, 3rd Floor
Autonomous Systems