The Architectural Implications of Autonomous Driving

Lin, Shih-Chieh; Zhang, Yunqi; Hsu, Chang-Hong; Skach, Matt; Haque, M. E.; Tang, Lingjia; Mars, Jason

doi:10.1145/3173162.3173191

Cited by 275 publications

(166 citation statements)

References 15 publications

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“…To enable cost-efficient operation with better QoS, resource management algorithms must be adaptive and perform management according to an applications nature. For example, more computing resources must be given to a self-driving car than AR to enable faster execution of the self-driving car functions to avoid accidents [151]. On the other hand, the throughput requirement of AR is larger than for self-driving cars [152].…”

Section: Resource Managementmentioning

confidence: 99%

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Khan

Yaqoob

Tran

et al. 2020

IEEE Internet Things J.

252

121

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Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

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Section: Resource Managementmentioning

confidence: 99%

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Khan

Yaqoob

Tran

et al. 2020

IEEE Internet Things J.

252

121

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“…The three main perception tasks are simultaneous localization and mapping (SLAM), object detection, and object tracking, which are all visual-based applications. Many studies take them as the vital parts in the autonomous driving pipeline [1], [19], [35]. Hence, we chose these three applications in the ADAS/AD scenario.…”

Section: A Methodology and Overviewmentioning

confidence: 99%

CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles

Wang

Liu²,

et al. 2018

2018 IEEE/ACM Symposium on Edge Computing (SEC)

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Connected and autonomous vehicles (CAVs) have recently attracted a significant amount of attention both from researchers and industry. Numerous studies targeting algorithms, software frameworks, and applications on the CAVs scenario have emerged. Meanwhile, several pioneer efforts have focused on the edge computing system and architecture design for the CAVs scenario and provided various heterogeneous platform prototypes for CAVs. However, a standard and comprehensive application benchmark for CAVs is missing, hindering the study of these emerging computing systems. To address this challenging problem, we present CAVBench, the first benchmark suite for the edge computing system in the CAVs scenario. CAVBench is comprised of six typical applications covering four dominate CAVs scenarios and takes four datasets as standard input. CAVBench provides quantitative evaluation results via application and system perspective output metrics. We perform a series of experiments and acquire three systemic characteristics of the applications in CAVBench. First, the operation intensity of the applications is polarized, which explains why heterogeneous hardware is important for a CAVs computing system. Second, all applications in CAVBench consume high memory bandwidth, so the system should be equipped with high bandwidth memory or leverage good memory bandwidth management to avoid the performance degradation caused by memory bandwidth competition. Third, some applications have worse data/instruction locality based on the cache miss observation, so the computing system targeting these applications should optimize the cache architecture. Last, we use the CAVBench to evaluate a typical edge computing platform and present the quantitative and qualitative analysis of the benchmarking results.

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“…Lin et al explored the hardware computing platform design of autonomous vehicles [70]. They chose three core applications on autonomous vehicles, which are localization, object detection, and object tracking, to run on heterogeneous hardware platform: GPUs, FPGAs, and ASICs.…”

Section: B Connected and Autonomous Vehiclesmentioning

confidence: 99%

OpenEI: An Open Framework for Edge Intelligence

Zhang

Wang

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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1 Abstract-In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands.However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.

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The Architectural Implications of Autonomous Driving

Cited by 275 publications

References 15 publications

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles

OpenEI: An Open Framework for Edge Intelligence

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