GPU Utilization: Predictive SARIMAX Time Series Analysis

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or Inception3 ). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX)

LiDAR Buoy Detection for Autonomous Marine Vessel Using Pointnet Classification

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game Engine (herein referred to as Unity) simulation data was used for the training and testing of a Pointnet neural network model while the labeled real-world maritime environment point cloud data was used for the model validation. Fitting the Pointnet model on the simulation data, after some data alignment with the LiDAR images allowed for accurate classification of buoys on the real-world data with the 93% of accuracy. A traditional image processing approach using 2D occupancy maps to detect the buoys by shape was used as well and is outlined in the paper