Stuck-at Faults Tolerance and Recovery in MLP Neural Networks Using Imperfect Emerging CNFET Technology

Devices using emerging technologies and materials with the potential to outperform their silicon counterpart are actively explored in search of ways to extend Moore's law. Among these technologies, low dimensional channel materials (LDMs) devices, such as carbon nanotube field-effect transistors (CNFETs), are promising to eventually outperform silicon CMOS. As these technologies are in their early development stages, their devices still suffer from high levels of defects and variations, thus unsuitable for nowadays general-purpose applications. On the other hand, applications with inherent error resilience and high-performance demands would suppress the impact of process imperfection and benefit from the performance boost. These applications, including image processing and machine learning through neural networks, would be the ideal targets for adopting these new emerging technologies even in their early stage of technology and process development. In this article, the effects of stuck-at faults in CNFET static random access memory (SRAM)-based multilayer perceptron (MLP) neural network are investigated. The impacts of various fault patterns are analyzed. Several fault recovery techniques are introduced, and their effectiveness is analyzed under different scenarios. With the proposed recovery techniques, the system can recover and tolerate a high level of stuck-at faults up to 40%, paving the path to adopt the early-stage and faulty emerging devices technologies in such high-demand applications.