主题演讲1:自动驾驶中弹性计算的道路是由冗余铺就的

2000 IEEE International Reliability Physics Symposium Proceedings. 38th Annual (Cat. No.00CH37059) Pub Date : 2018-03-01 DOI:10.1109/IRPS.2018.8353536

N. Saxena, S. Mathew, K. Saraswat

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引用次数: 1

摘要

深度神经网络在自动驾驶等应用中使用大规模并行处理器的计算能力。自动驾驶需要弹性(如安全性和可靠性)和每秒数万亿次的计算性能，以极高的精度处理传感器数据。本次主题演讲探讨了实现自动驾驶汽车弹性的各种方法，并提出了基于冗余设计多样性的案例。

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Keynote 1: The road to resilient computing in autonomous driving is paved with redundancy

Deep neural networks use the computational power of massively parallel processors in applications such as autonomous driving. Autonomous driving demands resiliency (as in safety and reliability) and trillions of operations per second of computing performance to process sensor data with extreme accuracy. This keynote examines various approaches to achieve resiliency in autonomous cars and makes the case for design diversity based redundancy.

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2000 IEEE International Reliability Physics Symposium Proceedings. 38th Annual (Cat. No.00CH37059)

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