The superior performance of Deep Neural Networks (DNNs) has led to their
application in various aspects of human life. Safety-critical applications are
no exception and impose rigorous reliability requirements on DNNs. Quantized
Neural Networks (QNNs) have emerged to tackle the complexity of DNN
accelerators, however, they are more prone to reliability issues.
In this paper, a recent analytical resilience assessment method is adapted
for QNNs to identify critical neurons based on a Neuron Vulnerability Factor
(NVF). Thereafter, a novel method for splitting the critical neurons is
proposed that enables the design of a Lightweight Correction Unit (LCU) in the
accelerator without redesigning its computational part.
The method is validated by experiments on different QNNs and datasets. The
results demonstrate that the proposed method for correcting the faults has a
twice smaller overhead than a selective Triple Modular Redundancy (TMR) while
achieving a similar level of fault resiliency.

本文使用一种分析抗干扰性的新方法来评估量化神经网络，并提出了一种轻量级修正单元，用于修复其关键神经元的错误，其开销比选择性三模冗余法小一半，而实现类似的容错能力。

通过有选择性的神经元分裂提高 QNN 的容错性

Enhancing Fault Resilience of QNNs by Selective Neuron Splitting

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays
widely used in many scenarios, including safety-critical applications such as
autonomous driving. In this context, besides energy efficiency and performance,
reliability plays a crucial role since a system failure can jeopardize human
life. As with any other device, the reliability of hardware architectures
running DNNs has to be evaluated, usually through costly fault injection
campaigns. This paper explores the approximation and fault resiliency of DNN
accelerators. We propose to use approximate (AxC) arithmetic circuits to
agilely emulate errors in hardware without performing fault injection on the
DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based
simulation framework. Further, we propose a fine-grain analysis of fault
resiliency by examining fault propagation and masking in networks

本文研究了 DNN 加速器的逼近计算和容错能力，提出使用逼近算术电路代替昂贵的故障注入检测，并开发了 GPU 模拟方法，同时通过网络故障的传播和掩蔽来实现精细化容错分析