DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

CyberLiveApp: a secure sharing and migration approach for live virtual desktop applications in a cloud environment

In recent years we have witnessed the rapid advent of cloud computing, in which the remote software is delivered as a service and accessed by users using a thin client over the Internet. In particular, the traditional desktop application can execute in the remote virtual machines without re-architecture providing a personal desktop experience to users through remote display technologies. However, existing cloud desktop applications mainly achieve isolation environments using virtual machines (VMs), which cannot adequately support application-oriented collaborations between multiple users and VMs. In this paper, we propose a flexible collaboration approach, named CyberLiveApp, to enable live virtual desktop applications sharing based on a cloud and virtualization infrastructure. The CyberLiveApp supports secure application sharing and on-demand migration among multiple users or equipment. To support VM desktop sharing among multiple users, a secure access mechanism is developed to distinguish view privileges allowing window operation events to be tracked to compute hidden window areas in real time. A proxy-based window filtering mechanism is also proposed to deliver desktops to different users. To support application sharing and migration between VMs, we use the presentation streaming redirection mechanism and VM cloning service. These approaches have been preliminary evaluated on an extended MetaVNC. Results of evaluations have verified that these approaches are effective and useful