An official website of the United States government
Emerging Multicore SoC SmartNICs, enclosing rich computing resources (e.g., a multicore processor, onboard DRAM, accelerators, programmable DMA engines), hold the potential to offload generic datacenter server tasks. However, it is unclear how to use a SmartNIC efficiently and maximize the offloading benefits, especially for distributed applications. Towards this end, we characterize four commodity SmartNICs and summarize the offloading performance implications from four perspectives: traffic control, computing capability, onboard memory, and host communication. Based on our characterization, we build iPipe, an actor-based framework for offloading distributed applications onto SmartNICs. At the core of iPipe is a hybrid scheduler, combining FCFS and DRR-based processor sharing, which can tolerate tasks with variable execution costs and maximize NIC compute utilization. Using iPipe, we build a real-time data analytics engine, a distributed transaction system, and a replicated key-value store, and evaluate them on commodity SmartNICs. Our evaluations show that when processing 10/25Gbps of application bandwidth, NIC-side offloading can save up to 3.1/2.2 beefy Intel cores and lower application latencies by 23.0/28.0 μs.
more »
« less
Go-CHART: A miniature remotely accessible self-driving car robot
The Go-CHART is a four-wheel, skid-steer robot that resembles a 1:28 scale standard commercial sedan. It is equipped with an onboard sensor suite and both onboard and external computers that replicate many of the sensing and computation capabilities of a full-size autonomous vehicle. The Go-CHART can autonomously navigate a small-scale traffic testbed, responding to its sensor input wiwithth programmed controllers. Alternatively, it can be remotely driven by a user who views the testbed through the robot's four camera feeds, which facilitates safe, controlled experiments on driver interactions with driverless vehicles. We demonstrate the Go-CHART's ability to perform lane tracking and detection of traffic signs, traffic signals, and other Go-CHARTs in real-time, utilizing an external GPU that runs computationally intensive computer vision and deep learning algorithms.
more »
« less
- Award ID(s):
- 1828010
- PAR ID:
- 10277361
- Date Published:
- Journal Name:
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 2265 to 2272
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A smart home with a controller that can understandand predict the interaction between the external environment and the user’s behavior and preferences can provide significant energy efficiency and savings. Unfortunately, experimentation of real world homes for the development of such a controller is prohibitively expensive. In this paper we describe techniques through which such experiments can be performed on scaled testbed with an accelerated time. We illustrate how the modeling of different geographical areas can be performed by the mapping of the model’s temperature and time to their real-world equivalents. We train three different machine learning models for predicting different sensor readings in the testbed, and find that the achieved predictive accuracy supports the feasibility of the development of future smart climate controllers.more » « less
-
There are various applications of Cyber-Physical systems (CPSs) that are life-critical where failure or malfunction can result in significant harm to human life, the environment, or substantial economic loss. Therefore, it is important to ensure their reliability, security, and robustness to the attacks. However, there is no widely used toolbox to simulate CPS and target security problems, especially the simulation of sensor attacks and defense strategies against them. In this work, we introduce our toolbox CPSim, a user-friendly simulation toolbox for security problems in CPS. CPSim aims to simulate common sensor attacks and countermeasures to these sensor attacks. We have implemented bias attacks, delay attacks, and replay attacks. Additionally, we have implemented various recovery-based methods against sensor attacks. The sensor attacks and recovery methods configurations can be customized with the given APIs. CPSim has built-in numerical simulators and various implemented benchmarks. Moreover, CPSim is compatible with other external simulators and can be deployed on a real testbed for control purposes.1more » « less
-
We present the first system that can airdrop wireless sensors from small drones and live insects. In addition to the challenges of achieving low-power consumption and long-range communication, airdropping wireless sensors is difficult because it requires the sensor to survive the impact when dropped in mid-air. Our design takes inspiration from nature: small insects like ants can fall from tall buildings and survive because of their tiny mass and size. Inspired by this, we design insect-scale wireless sensors that come fully integrated with an onboard power supply and a lightweight mechanical actuator to detach from the aerial platform. Our system introduces a first-of-its-kind 37 mg mechanical release mechanism to drop the sensor during flight, using only 450 μJ of energy as well as a wireless communication link that can transmit sensor data at 33 kbps up to 1 km. Once deployed, our 98 mg wireless sensor can run for 1.3-2.5 years when transmitting 10-50 packets per hour on a 68 mg battery. We demonstrate attachment to a small 28 mm wide drone and a moth (Manduca sexta) and show that our insect-scale sensors flutter as they fall, suffering no damage on impact onto a tile floor from heights of 22 m.more » « less
-
The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera. Bespoke localization and tracking algorithms are created for the LIDAR and cameras. In total, there are 26 different scenarios of the test vehicles navigating the testbed intersection; for this work, we are only considering car following scenarios. The LIDAR data collected from the infrastructure-based and onboard vehicle-based sensors system are used to perform object detection and multi-target tracking to estimate the velocity and position information of the test vehicles and use these values to compute OSA metrics. The comparison of the performance of the two systems involves the localization and tracking errors in calculating the position and the velocity of the subject vehicle, with the real-time differential GPS data serving as ground truth for velocity comparison and tracking results from the drone for OSA metrics comparison.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS45743.2020.9341770
