Device and materials requirements for neuromorphic computing. (18th January 2019) - Islam, Raisul; Li, Haitong; Chen, Pai-Yu; Wan, Weier; Chen, Hong-Yu; Gao, Bin; Wu, Huaqiang; Yu, Shimeng; Saraswat, Krishna; Philip Wong, H-S
Abstract: Energy efficient hardware implementation of artificial neural network is challenging due the 'memory-wall' bottleneck. Neuromorphic computing promises to address this challenge by eliminating data movement to and from off-chip memory devices. Emerging non-volatile memory (NVM) devices that exhibit gradual changes in resistivity are a key enabler of in-memory computing—a type of neuromorphic computing. In this paper, we present a review of some of the NVM devices (RRAM, CBRAM, PCM) commonly used in neuromorphic application. The review focuses on the trade-off between device parameters such as retention, endurance, device-to-device variation, speed and resistance levels, and the interplay with target applications. This work aims at providing guidance for finding the optimized resistive memory devices material stack suitable for neuromorphic application.