Comprehensive Reliability Analysis and Fault-Tolerant Approaches for Vision Transformers

Core Concepts

Vision Transformers are generally more resilient to soft errors in linear computing like GEMM and FC, but more fragile in non-linear computing like softmax and GELU compared to typical CNNs. A lightweight block-wise ABFT approach and a range-based protection scheme can significantly improve the accuracy of Vision Transformers in the presence of soft errors.

Abstract

The paper presents a comprehensive reliability analysis of Vision Transformers (ViTs) from different granularities including models, layers, modules, and patches. The key findings are:

ViTs are generally more resilient to soft errors compared to CNNs, but the non-linear functions like softmax and GELU in ViTs pose more negative influence on the model accuracy in the presence of soft errors.
The vulnerability factors across the input patches of ViTs are relatively evenly distributed, which differs substantially from the center-biased vulnerability in CNNs.

Based on the reliability analysis, the authors propose two fault-tolerant approaches:

A lightweight block-wise algorithm-based fault tolerance (LB-ABFT) approach to protect the linear computing (GEMM and FC) in ViTs. It adaptively selects the optimal block size for GEMM based on the error rate to minimize the computing overhead.
A range-based protection scheme to mitigate soft errors in the non-linear computing (softmax and GELU) of ViTs.

The proposed fault-tolerant approaches can significantly improve the accuracy of ViTs in the presence of various soft errors, while incurring minor computing overhead.

Stats

The top-1 accuracy of ViT-B drops from 82.1% to 0% when the bit error rate (BER) increases from 1E-10 to 2E-8.
The top-1 accuracy of Swin-T drops from 81.3% to 0% when the BER increases from 7E-10 to 2E-8.
The top-1 accuracy of DeepViT-S drops from 79.6% to 0% when the BER increases from 1E-9 to 2E-8.
The top-1 accuracy of CaiT-XXS-24 drops from 77.8% to 0% when the BER increases from 2E-9 to 2E-8.

Quotes

"ViTs with the self-attention mechanism are generally more resilient on linear computing including general matrix-matrix multiplication (GEMM) and full connection (FC) and show a relatively even vulnerability distribution across the patches."
"ViTs involve more fragile non-linear computing such as softmax and GELU compared to typical CNNs."

Key Insights Distilled From

Soft Error Reliability Analysis of Vision Transformers

by Xinghua Xue,... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2302.10468.pdf

Deeper Inquiries

How can the proposed fault-tolerant approaches be extended to protect other types of neural networks beyond Vision Transformers

The proposed fault-tolerant approaches can be extended to protect other types of neural networks beyond Vision Transformers by adapting the protection strategies to the specific characteristics of those networks. For instance, for convolutional neural networks (CNNs), which are widely used in image recognition tasks, the fault-tolerant approaches can be modified to focus on protecting the convolutional layers and pooling operations. By identifying the most vulnerable components in CNNs and applying similar fault detection and recovery mechanisms, the reliability of CNNs can be enhanced in the presence of soft errors. Additionally, for recurrent neural networks (RNNs) used in sequential data processing tasks, the fault-tolerant approaches can be tailored to protect the recurrent connections and activation functions. By customizing the fault-tolerant strategies to suit the unique architecture and operations of different neural network types, a comprehensive protection framework can be established to improve the resilience of various neural network models.

What are the potential limitations of the operation-wise fault injection framework used in this work, and how can it be improved to better model real-world soft errors

The operation-wise fault injection framework used in this work has certain limitations that can be addressed to better model real-world soft errors. One limitation is the assumption of an even error distribution across operations, which may not accurately reflect the actual error propagation patterns in neural networks. To improve this, a more realistic error distribution model based on empirical data or fault injection experiments can be developed to capture the varying impact of errors on different operations. Additionally, the framework could benefit from incorporating probabilistic models to simulate the stochastic nature of soft errors more effectively. By introducing probabilistic fault injection techniques, the framework can better mimic the random occurrence of soft errors in hardware components. Furthermore, enhancing the framework to consider the temporal aspects of errors, such as error propagation over time and error accumulation effects, can provide a more comprehensive understanding of the reliability challenges faced by neural networks in real-world scenarios.

Given the differences in reliability between linear and non-linear operations, how can the model architecture be further optimized to improve the overall soft error resilience of Vision Transformers

To further optimize the model architecture and improve the overall soft error resilience of Vision Transformers, several strategies can be implemented. One approach is to introduce redundancy at critical points in the network, such as incorporating redundant pathways or redundant computations to mitigate the impact of soft errors. By duplicating key components or operations within the network, the system can better withstand errors and maintain accuracy. Another optimization technique is to implement error detection and correction mechanisms within the network itself, enabling the model to identify and rectify errors in real-time. This proactive approach can help prevent error propagation and minimize accuracy degradation. Additionally, exploring adaptive fault-tolerant strategies that dynamically adjust the level of protection based on the current error rate or system conditions can enhance the efficiency and effectiveness of the fault-tolerant mechanisms. By continuously monitoring the error rate and adapting the protection mechanisms accordingly, Vision Transformers can achieve robust performance in the presence of soft errors.