Abstract
This chapter details the utilization of RRAM devices as key components in neuromorphic computing for efficient in-memory computing. Beginning with the fundamental mechanism of RRAM and its data storage capabilities and followed by efficient AI implementations with RRAM. This includes discussions on RRAM-based accelerators facilitating DNN computations with remarkable O(1) time complexity efficiency, as well as the RRAM’s multi-level characteristics. Subsequently, the chapter addresses challenges encountered in RRAM technology, such as variations, IR-drop issues, and the substantial energy and area requirements associated with DAC/ADC operations. Solutions to these challenges are briefly summarized. Emphasis is then placed on the critical issue of programming RRAM devices, with challenges including cycle-to-cycle variation and energy-intensive processes. Various programming techniques are explicated, accompanied by a comparative analysis of their respective advantages and drawbacks.