ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs

• URL: http://arxiv.org/abs/2406.06313v1
• Date: Mon, 10 Jun 2024 14:31:38 GMT
• Title: ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs
• Authors: Seyedhamidreza Mousavi, Mohammad Hasan Ahmadilivani, Jaan Raik, Maksim Jenihhin, Masoud Daneshtalab,
• Abstract summary: State-of-the-art methods offer either neuron-wise or layer-wise clipping activation functions. Layer-wise clipped activation functions cannot preserve DNNs resilience at high bit error rates. We propose a hybrid clipped activation function that integrates neuron-wise and layer-by-layer methods.
• Score: 0.4660328753262075
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs against hardware faults, activation restriction techniques significantly mitigate the fault effects at the DNN structure level, irrespective of accelerator architectures. State-of-the-art methods offer either neuron-wise or layer-wise clipping activation functions. They attempt to determine optimal clipping thresholds using heuristic and learning-based approaches. Layer-wise clipped activation functions cannot preserve DNNs resilience at high bit error rates. On the other hand, neuron-wise clipping activation functions introduce considerable memory overhead due to the addition of parameters, which increases their vulnerability to faults. Moreover, the heuristic-based optimization approach demands numerous fault injections during the search process, resulting in time-consuming threshold identification. On the other hand, learning-based techniques that train thresholds for entire layers concurrently often yield sub-optimal results. In this work, first, we demonstrate that it is not essential to incorporate neuron-wise activation functions throughout all layers in DNNs. Then, we propose a hybrid clipped activation function that integrates neuron-wise and layer-wise methods that apply neuron-wise clipping only in the last layer of DNNs. Additionally, to attain optimal thresholds in the clipping activation function, we introduce ProAct, a progressive training methodology. This approach iteratively trains the thresholds on a layer-by-layer basis, aiming to obtain optimal threshold values in each layer separately.

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