Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge

• URL: http://arxiv.org/abs/2609.16742v1
• Date: Tue, 15 Sep 2026 07:15:30 GMT
• Title: Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge
• Abstract summary: We propose carry-through, a fundamentally new scheme for soft-error detection in CNN inference on embedded GPU.<n>The proposed method detects 95.86% and 86.56% of critical faults for FP32 and FP16, respectively, at almost no additional per-image overhead.
• Score: 0.21748200848556343
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Convolutional Neural Networks (CNNs) are increasingly deployed in safety-critical edge applications, where soft errors can silently corrupt inference outputs and lead to unsafe decisions. Such applications typically rely on resource-constrained embedded GPUs, requiring fault detection and mitigation techniques that add minimal compute, memory, and latency overhead while integrating seamlessly with the standard GPU inference pipeline. Existing algorithm-based fault tolerance techniques rely on matrix augmentation and per-operation checksum verification, imposing substantial overhead that is prohibitive for CNN inference on embedded GPUs. In this work, we propose carry-through checksum, a fundamentally new scheme for soft-error detection in CNN inference on embedded GPUs. The method embeds dedicated carry-through filters into the convolutional layers, which compute a checksum from the CNN's own operations and propagate it through inference, enabling end-to-end error detection with a single output verification. Experimental results on multiple CNN architectures show that the proposed method detects 95.86% and 86.56% of critical faults for FP32 and FP16, respectively, at almost no additional per-image overhead. Detected faults are mitigated through re-execution, incurring only 2.27% run-time overhead across the entire test set on an NVIDIA Jetson Orin NX GPU.

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