Early Soft Error Reliability Assessment of Convolutional Neural Networks Executi

Early Soft Error Reliability Assessment of Convolutional Neural Networks Executi

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Soft errors, also known as transient faults, pose a significant threat to the reliability of hardware systems, including Field Programmable Gate Arrays (FPGAs) used for executing Convolutional Neural Networks (CNNs). These errors can occur due to various factors such as cosmic radiation, electrical noise, and manufacturing defects, leading to incorrect computations and potentially compromising the accuracy of the neural network.

In order to ensure the reliability of CNN execution on FPGA platforms, it is crucial to perform early assessment of soft error vulnerability. This involves evaluating the susceptibility of the hardware to soft errors and implementing mitigation strategies to minimize their impact on the network’s performance.

One approach to assessing soft error reliability is to use fault injection techniques to simulate the effects of soft errors on the FPGA. By introducing faults into the system and monitoring the network’s behavior, researchers can identify potential weak points and develop strategies to improve fault tolerance.

In addition to fault injection, researchers can also leverage tools such as fault-tolerant design techniques and error detection and correction algorithms to enhance the reliability of CNN execution on FPGA platforms.

Overall, early soft error reliability assessment is essential for ensuring the robustness of CNNs executed on FPGAs, particularly in safety-critical applications where accuracy and reliability are paramount. By proactively addressing soft error vulnerabilities, researchers can improve the overall reliability and performance of neural networks on FPGA platforms.
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