An Evaluation of the NVIDIA TX1 for Supporting Real-Time Computer-Vision Workloads

Otterness, Nathan; Yang, Ming–Hsuan; Rust, Sarah; Park, Eunbyung; Anderson, James H.; Smith, F. Donelson; Berg, Alex; Wang, Shige

doi:10.1109/rtas.2017.3

Cited by 91 publications

(38 citation statements)

References 22 publications

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“…Real-time embedded systems, such as an autonomous vehicle, present unique challenges for deep learning, as the computing platforms of such systems must satisfy two often conflicting goals: (1) The platform must provide enough computing capacity for real-time processing of computationally expensive AI workloads (deep neural networks); and (2) The platform must also satisfy various constraints such as cost, size, weight, and power consumption limits [25].…”

Section: B Embedded Computing Platforms For Real-time Inferencingmentioning

confidence: 99%

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DeepPicar: A Low-Cost Deep Neural Network-Based Autonomous Car

Bechtel

Mcellhiney

Kim

et al. 2018

2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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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.

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Section: B Embedded Computing Platforms For Real-time Inferencingmentioning

confidence: 99%

“…On the other hand, the computing hardware platform must also satisfy cost, size, weight, and power constraints, which require a highly efficient computing platform. These two conflicting requirements complicate the platform selection process as observed in [25].…”

Section: Introductionmentioning

confidence: 99%

DeepPicar: A Low-Cost Deep Neural Network-Based Autonomous Car

Bechtel

Mcellhiney

Kim

et al. 2018

2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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“…Integrated GPU based platforms have recently gained much attention in the real-time systems community. In [2], [12], the authors investigate the suitability of NVIDIA's Tegra X1 platform for use in safety critical real-time systems. With careful reverse engineering, they have identified undisclosed scheduling policies that determine how concurrent GPU kernels are scheduled on the platform.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Simultaneously coscheduling multiple kernels is called GPU co-scheduling, which has been avoided in most prior real-time GPU management approaches [8], [10], [11] as well due to unpredictable timing. According to [2], preventing GPU co-scheduling does not necessarily hurt-if not improve-performance because con-current GPU kernels from different tasks are executed in a timemultiplexed manner rather than being executed in parallel. 2 Executing GPU kernels typically requires copying considerable amount of data between the CPU and the GPU.…”

Section: System Modelmentioning

confidence: 99%

“…These intelligent robots are, however, resource constrained real-time embedded systems that not only require high computing performance but also must satisfy a variety of constraints such as size, weight, power consumption (SWaP) and cost. This makes integrated CPU-GPU architecture based computing platforms, which integrate CPU and GPU in a single chip (e.g., NVIDIA's Jetson [1] series), an appealing solution for such robotics applications because of their high performance and efficiency [2].…”

Section: Introductionmentioning

confidence: 99%

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Work-In-Progress: Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

Ali

Yun

2017

2017 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Integrated CPU-GPU architecture provides excellent acceleration capabilities for data parallel applications on embedded platforms while meeting the size, weight and power (SWaP) requirements. However, sharing of main memory between CPU applications and GPU kernels can severely affect the execution of GPU kernels and diminish the performance gain provided by GPU. For example, in the NVIDIA Jetson TX2 platform, an integrated CPU-GPU architecture, we observed that, in the worst case, the GPU kernels can suffer as much as 3X slowdown in the presence of co-running memory intensive CPU applications. In this paper, we propose a software mechanism, which we call BWLOCK++, to protect the performance of GPU kernels from co-scheduled memory intensive CPU applications.

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A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.

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An Evaluation of the NVIDIA TX1 for Supporting Real-Time Computer-Vision Workloads

Cited by 91 publications

References 22 publications

DeepPicar: A Low-Cost Deep Neural Network-Based Autonomous Car

DeepPicar: A Low-Cost Deep Neural Network-Based Autonomous Car

Work-In-Progress: Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

A survey on graphic processing unit computing for large‐scale data mining

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