Design Methodology for Energy Efficient Unmanned Aerial Vehicles

TL;DR

This paper introduces a load-balancing and energy-efficient methodology for UAV perception and navigation code, optimizing parallel execution on Network-on-chip architectures to significantly reduce energy consumption and improve performance.

Contribution

It presents a novel approach combining data dependency analysis, scheduling, and energy-aware mapping for efficient UAV onboard processing.

Findings

Achieved 82% energy savings in UAV processing.

Realized 4.7x performance speedup over existing flight controllers.

Validated on a drone self-navigation application.

Abstract

In this paper, we present a load-balancing approach to analyze and partition the UAV perception and navigation intelligence (PNI) code for parallel execution, as well as assigning each parallel computational task to a processing element in an Network-on-chip (NoC) architecture such that the total communication energy is minimized and congestion is reduced. First, we construct a data dependency graph (DDG) by converting the PNI high level program into Low Level Virtual Machine (LLVM) Intermediate Representation (IR). Second, we propose a scheduling algorithm to partition the PNI application into clusters such that (1) inter-cluster communication is minimized, (2) NoC energy is reduced and (3) the workloads of different cores are balanced for maximum parallel execution. Finally, an energy-aware mapping scheme is adopted to assign clusters onto tile-based NoCs. We validate this approach with a drone self-navigation application and the experimental results show that our optimal 32-core design achieves an average 82% energy savings and 4.7x performance speedup against the state-of-art flight controller.