An autonomous vehicle generates several terabytes of sensor data per day. A significant portion of this data consists of 3D point clouds produced by depth sensors such as LiDAR. This data is transferred to cloud storage, where it is utilized for training machine learning models or conducting analyses, e.g., forensic investigations in the event of an accident. To reduce network and storage costs, this paper introduces DejaView that searches for and uses redundancies on larger temporal scales (days and months) for more effective compression. We designed DejaView with the insight that the operating area of autonomous vehicles is limited and that vehicles mostly traverse the same routes daily. Consequently, the daily collected 3D data is likely similar to the data they’ve captured in the past. To capture this, the core of DejaView is a diff operation that compactly represents point clouds as delta w.r.t. 3D data from the past. Using two months of LiDAR data, DejaView can compress point clouds by a factor of 210 at a reconstruction error of only 15 cm.

Deep learning models are extensively used in a wide range of domains, e.g., scientific simulations, predictions, and modeling. However, training these dense networks is both compute and memory intensive, and typically requires accelerators such as Graphics Processing Units (GPUs). While such DNN workloads consume a major proportion of the limited onboard high-bandwidth memory (HBM), they typically underutilize the GPU compute resources. In such scenarios, the idle compute resources on the GPU can be leveraged to run pending jobs that can either be (1) accommodated on the remainder HBM, or (2) can share memory resources with other concurrent workloads. However, state-of-the-art workload schedulers and DNN runtimes are not designed to leverage HBM co-location to improve resource utilization and throughput. In this work, we propose COLTI, which introduces a set of novel techniques to solve the aforementioned challenges by co-locating DNN training and inference on memory-constrained GPU devices. Our preliminary evaluations of three different DNN models implemented in the PyTorch framework demonstrate up to 37% and 40% improvement in makespan and memory utilization, respectively.