Forward stagewise regression with multilevel memristor for sparse coding

Abstract Sparse coding is a prevalent method for image inpainting and feature extraction, which can repair corrupted images or improve data processing efficiency, and has numerous applications in computer vision and signal processing. Recently, several memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding remarkably. However, the variations and low precision of the devices will deteriorate the dictionary, causing inevitable degradation in the accuracy and reliability of the application. In this work, a digital-analog hybrid memristive sparse coding system is proposed utilizing a multilevel Pt/Al 2 O 3 /AlO x /W memristor, which employs the forward stagewise regression algorithm: The approximate cosine distance calculation is conducted in the analog part to speed up the computation, followed by high-precision coefficient updates performed in the digital portion. We determine that four states of the aforementioned memristor are sufficient for the processing of natural images. Furthermore, through dynamic adjustment of the mapping ratio, the precision requirement for the digit-to-analog converters can be reduced to 4 bits. Compared to the previous system, our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ratio. Moreover, in the context of image inpainting, images containing 50% missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.