Robustness for Embedded Machine Learning Using In-Memory Computing

Deep Neural Networks (DNNs) have achieved superhuman-like performance in several real-world applications such as classification, segmentation among others. Recently, analog crossbar architectures have been proposed as a viable in-memory computing alternative to improve the compute efficiency of DNNs for low power embedded applications. Although DNNs have achieved high performance, recent works have shown that they are vulnerable to adversarial attacks where small, imperceptible noise added to the input data can degrade the DNN performance. To this end, prior works have proposed algorithmic strategies such as adversarial classification and detection to mitigate the effect of adversarial attacks. However, these approaches are not energy-efficient and suffer from performance degradation when naively implemented on analog crossbars having various non-idealities inherently. To this end, in this chapter, we highlight efficiency-driven analog crossbar-aware approaches to improve the robustness of DNNs in two broad aspects: (1) Improving adversarial robustness of crossbar-mapped DNNs wherein, we discuss two recent works—NEAT and DetectX. (2) Examining and improving the natural robustness of crossbar-mapped structure-pruned DNN models against non-idealities.