Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Tabani, Hamid; Mazzocchetti, Fabio; Benedicte, Pedro; Abella, Jaume; Cazorla, Francisco J.

doi:10.1016/j.jpdc.2021.02.008

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…The use of more efficient model construction procedures can be devised, by using different feature space reduction such as system identification [79] and unsupervised learning [80], or taking advantage of the behavior of the models analyzed herein concerning the performance on different error amplitudes for the construction of model ensembles [81,82]. For the real-time implementation of such frameworks in dedicated embedded hardware, FPGAs [83][84][85] and GPUs [86][87][88][89] have delivered important results recently which may also be used in the context of stress estimation to provide feasible implementations for practical applications.…”

Section: Discussionmentioning

confidence: 99%

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

2021

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Background Due to the acoustoelastic effect, ultrasonic guided waves have been used to estimate mechanical stress in a cheap and nondestructive fashion. Machine learning has been applied to map complex waveforms to stress estimates, though important aspects to construct and deploy real-time monitoring systems, such as accuracy and hardware consumption, to date, have not been concomitantly explored. Objective The goal of the present paper is to propose a data modeling methodology that optimizes accuracy and computational implementation, towards devising the best practices for real-time ultrasonic-based stress estimation. Methods We evaluate shallow and deep learning models with dimensionality reduction, which are compared to the most recent work showing overall better results for the approach presented herein both in terms of accuracy and hardware resources consumption. We generated a dataset to evaluate the models on a test rig with a plate measuring (i) ultrasonic guided waves excited by broadband signals and (ii) different stress conditions. The models are created and tested using a Monte-Carlo hold-out procedure with a repeated k-fold randomized hyperparameter search to evaluate their robustness in different stress conditions. ResultsWe dramatically improve the current state of the art by proposing and evaluating a more efficient model construction procedure. Results show that by using shallow models and principal component analysis, we were able to improve the model performance in terms of predictive power and inference hardware consumption. Specifically, we obtained an accuracy improvement of about 10% and hardware consumption used for inference smaller in three orders of magnitude when compared to the state-of-the-art reported with deep neural network models. Conclusions Results herein depicted impact the way practitioners may proceed for building efficient embedded predictive models for stress estimation using guided waves, enabling more precise, decentralized, and real-time nondestructive monitoring.

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Section: Discussionmentioning

confidence: 99%

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

2021

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“…There are many embedded platforms for edge computing, such as Raspberry Pi [33], Intel Movidius Neural Compute Stick (NCS) [34], and Nvidia's Jetson Nano and Jetson AGX Xavier [35]. We evaluate each edge computing platform to finally select one that meets the algorithm requirements and data processing volume.…”

Section: Edge Computing Modulementioning

confidence: 99%

A Multidimensional Data Collection and Edge Computing Analysis Method

Ji,

Li,

Zhao

et al. 2023

Applied Sciences

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With the development of IoT technology, many dimensions of data are generated in the environment where we live. The study of these data is critical to our understanding of the relationships between people and between people and cities. The core components of IoT technology are sensors and control circuits. However, merging various sensor data and real-time data processing is often a difficult problem, usually related to factors such as coverage, lighting conditions, and accuracy of object detection. Therefore, we firstly propose a wireless transmission hardware architecture for data acquisition mainly based on vision sensors, and at the same time, incorporate some sensors for data calibration to improve the accuracy of data detection. The collected data are fed back to the edge computing platform for fast processing. The edge platform is designed with a lightweight target detection model and data analysis model. Through this multidimensional data collection and analysis, a generalised functional model for public space utilization can be fitted, which enables the calculation of utilization rates for any parameter in public space. The technology improves a technical reference for multi-dimensional data collection and analysis.

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“…ADBench provides realistic benchmarks in terms of functionality and complexity with respect to industry-level AD systems. Also ADBench targets realistic hardware platforms for its execution and provides benchmarks compatible with the latest heterogeneous hardware platforms for AD systems such as NVIDIA Drive Xavier [6] and NVIDIA Drive Pegasus [7].…”

Section: Introductionmentioning

confidence: 99%

ADBench: benchmarking autonomous driving systems

et al. 2021

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Driven by the improvements in a variety of domains, autonomous driving is becoming a reality and today, industry aims at moving toward fully autonomous vehicles. High-tech chip manufacturers are designing highperformance and energy-efficient platforms in accordance with safety standard requirements. However, the software used to implement advanced functionalities in autonomous vehicles challenges real-time constraints on those platforms. Hence, there is a clear need for industry-level autonomous driving benchmarks to evaluate platforms and systems. In this paper, we propose ADBench, a benchmarking approach and benchmark suite for state-of-the-art autonomous driving platforms, in accordance with the key modules, structural design and functions of AD systems, building on several industry-level autonomous driving systems. The use of standard benchmarks facilitates the design, verification and validation process of autonomous systems.

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Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Cited by 16 publications

References 20 publications

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

A Multidimensional Data Collection and Edge Computing Analysis Method

ADBench: benchmarking autonomous driving systems

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