Automated design of error-resilient and hardware-efficient deep neural networks
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ORIGINAL ARTICLE
Automated design of error-resilient and hardware-efficient deep neural networks Christoph Schorn1,2 • Thomas Elsken3,4 • Sebastian Vogel1,2 • Armin Runge1 • Andre Guntoro1 Gerd Ascheid2
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Received: 17 June 2019 / Accepted: 25 April 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. The design of the neural architecture has a large influence on the achievable efficiency and bit error resilience of the network on hardware. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multiobjective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of inexpensively computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find one providing a significantly higher error resilience compared to the other. Finally, a comparison of the architectures provided by our algorithm with the popular MobileNetV2 and NASNet-A models reveals an up to seven times improved bit error resilience of our models. We are the first to combine error resilience, efficiency, and performance optimization in a neural architecture search framework. Keywords Neural network hardware Error resilience Hardware faults Neural architecture search Multi-objective optimization AutoML
1 Introduction The application of deep neural networks (DNNs) in safetycritical perception systems, for example autonomous vehicles (AVs), poses some challenges on the design of the underlying hardware platforms. On the one hand, efficient and fast accelerators are needed, since DNNs for computer & Christoph Schorn [email protected] 1
Bosch Corporate Research, Robert Bosch GmbH, Renningen, Germany
2
Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany
3
Bosch Center for Artificial Intelligence, Robert Bosch GmbH, Renningen, Germany
4
Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany
vision exhibit massive computational requirements [56]. On the other hand, resilience against random hardware faults has to be ensured. In many driving scenarios, entering a fail-safe state is not sufficient, but fail-operational behavior and fault tolerance are required [48]. However, fault tolerance techniques at the hardware level often entail large redunda
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