MulBERRY: Enabling Bit-Error Robustness for Energy-Efficient Multi-Agent Autonomous Systems

Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan, Pin-Yu Chen, Kshitij Bhardwaj, Vijay Janapa Reddi, Arijit Raychowdhury

Abstract: The adoption of autonomous swarms, consisting of a multitude of unmanned aerial vehicles (UAVs), operating in a collaborative manner, has become prevalent in mainstream application domains for both military and civilian purposes. These swarms are expected to collaboratively carry out navigation tasks and employ complex reinforcement learning (RL) models within the stringent onboard size, weight, and power constraints. While techniques such as reducing onboard operating voltage can improve the energy efficiency of both computation and flight missions, they can lead to on-chip bit failures that are detrimental to mission safety and performance.To this end, we propose MulBERRY, a multi-agent robust learning framework to enhance bit error robustness and energy efficiency for resource-constrained autonomous UAV swarms. MulBERRY supports multi-agent robust learning, both offline and on-device, with adaptive and collaborative agent-server optimizations. For the first time, MulBERRY demonstrates the practicality of robust low-voltage operation on multi-UAV systems leading to energy savings in both compute and mission quality-of-flight. We conduct extensive system-level experiments on autonomous multi-UAV navigation by leveraging algorithm-level robust learning techniques, and hardware-level bit error, thermal, and power characterizations. Through evaluations, we demonstrate that MulBERRY achieves robustness-performance-efficiency co-optimizations for autonomous UAV swarms. We also show that MulBERRY generalizes well across fault patterns, environments, UAV types, UAV agent numbers, and RL policies, with up to 18.97% reduction in flight energy, 22.07% increase in the number of successful missions, and 4.16× processing energy reduction.