节能型无人机设计方法
Design Methodology for Energy Efficient Unmanned Aerial Vehicles
摘要：
在本文中，我们提出了一种负载平衡的方法来分析和划分无人机感知和导航智能（PNI）代码并行执行，以及分配每个并行计算任务的处理单元在片上网络（NoC）架构，使总的通信能量最小化，减少拥塞。首先，我们通过将PNI高级程序转换为低级虚拟机（LLVM）中间表示（IR）来构造数据依赖图（DDG）。其次，我们提出了一个调度算法，分区PNI应用程序到集群，使（1）集群间的通信最小化，（2）NoC的能量减少和（3）不同的核心的工作负载平衡最大的并行执行。最后，采用能量感知的映射方案将簇分配到基于瓦片的NoC上。我们用无人机自主导航应用验证了这种方法，实验结果表明，我们的最佳32核设计与最先进的飞行控制器相比，平均节省了82%的能源，性能提高了4.7倍。
英文摘要：
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.
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