CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion

TL;DR

CARMA is a dynamic, context-aware sensor fusion system for autonomous systems that reconfigures FPGA-based components at runtime to improve energy efficiency and perception performance in real-world scenarios.

Contribution

It introduces a novel runtime reconfiguration approach using context-awareness and FPGA clock-gating to optimize energy use without sacrificing perception accuracy.

Findings

Up to 1.3x speedup in perception tasks.

73% reduction in energy consumption.

Effective adaptation to real-world sensing contexts.

Abstract

Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learning models, and powerful hardware platforms to perceive and safely operate in real-time. However, in many contexts, some sensing modalities negatively impact perception while increasing the system's overall energy consumption. Since AS are often energy-constrained edge devices, energy-efficient sensor fusion methods have been proposed. However, existing methods either fail to adapt to changing scenario conditions or to optimize energy efficiency system-wide. We propose CARMA: a context-aware sensor fusion approach that uses context to dynamically reconfigure the computation flow on a Field-Programmable Gate Array (FPGA) at runtime. By clock-gating unused sensors and model sub-components, CARMA significantly reduces the energy used by a multi-sensory object detector without compromising performance. We use a Deep-learning Processor Unit (DPU) based reconfiguration approach to minimize the latency of model reconfiguration. We evaluate multiple context-identification strategies, propose a novel system-wide energy-performance joint optimization, and evaluate scenario-specific perception performance. Across challenging real-world sensing contexts, CARMA outperforms state-of-the-art methods with up to 1.3x speedup and 73% lower energy consumption.