Automated design of error-resilient and hardware-efficient deep neural networks

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), multi-objective 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.