Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing

Dec 1, 2023 by A. Vasilopoulos, J. Büchel, B. Kersting, C. Lammie, K. Brew, Samuel Choi, T. Philip, N. Saulnier, Vijay Narayanan, M. Le Gallo, A. Sebastian (IEEE Transactions on Electron Devices)

Analog in- memory computing (AIMC) using memristive devices is considered a promising Non-von Neumann approach for deep learning (DL) inference tasks. However, inaccuracies in the programming of devices, that are attributed to conductance variations, pose a key challenge toward achieving sufficient compute precision for DL inference. Fortunately, conduction variations in memristive devices, such as phase-change memory (PCM) devices, exhibit a strong state dependence.