Presentation
RM-CIMA: An Analog Computing-in-Memory Accelerator for a Robust UAV Trajectory Tracking Framework
DescriptionUAV trajectory tracking demands robustness and real-time performance, but traditional architectures suffer high latency, failing to counter sudden disturbances. We propose an algorithm-hardware co-design framework to address this. We integrate reinforcement learning (RL) with model predictive control (MPC) for real-time online learning. Hardware-wise, we designed a dedicated analog compute-in-memory (ACIM) accelerator, RM-CIMA, mapping both RL learning and MPC optimization to the analog domain. RM-CIMA enables UAVs to counter fast disturbances, improving recovery times from strong wind by 7.9× over traditional NMPC. Furthermore, it reduces computational latency and energy consumption by 2995.6× and 983.1×, respectively, compared to traditional architectures.
Event Type
Research Manuscript
TimeWednesday, July 291:30pm - 1:42pm PDT
LocationMtg Room 203C