FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions

• URL: http://arxiv.org/abs/2112.13544v1
• Date: Mon, 27 Dec 2021 07:07:50 GMT
• Title: FitAct: Error Resilient Deep Neural Networks via Fine-Grained Post-Trainable Activation Functions
• Authors: Behnam Ghavami, Mani Sadati, Zhenman Fang, and Lesley Shannon
• Abstract summary: Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In this paper, we propose FitAct, a low-cost approach to enhance the error resilience of DNNs by deploying fine-grained post-trainable activation functions.
• Score: 0.05249805590164901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In such DNN-based systems, error resilience is a top priority since faults in DNN inference could lead to mispredictions and safety hazards. For latency-critical DNN inference on resource-constrained edge devices, it is nontrivial to apply conventional redundancy-based fault tolerance techniques. In this paper, we propose FitAct, a low-cost approach to enhance the error resilience of DNNs by deploying fine-grained post-trainable activation functions. The main idea is to precisely bound the activation value of each individual neuron via neuron-wise bounded activation functions so that it could prevent fault propagation in the network. To avoid complex DNN model re-training, we propose to decouple the accuracy training and resilience training and develop a lightweight post-training phase to learn these activation functions with precise bound values. Experimental results on widely used DNN models such as AlexNet, VGG16, and ResNet50 demonstrate that FitAct outperforms state-of-the-art studies such as Clip-Act and Ranger in enhancing the DNN error resilience for a wide range of fault rates while adding manageable runtime and memory space overheads.

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