Analog in-memory computing (AIMC) is an emerging paradigm that can enable energy efficient computing orders of magnitude beyond what is currently possible. Memory candidates for AIMC include SONOS (semiconductor oxide nitride oxide nitride), emerging resistive memory (ReRAM) and electrochemical memory (ECRAM). Electrical requirements for these memories are different than traditional digital memories in that the exact conductivity state of every device is used in every calculation. Effects including programming error and state drift are incorporated in the algorithm output. This new set of requirements has forced the development of a novel, holistic methodology for the electrical characterization and benchmarking of these devices. This talk will discuss these characterization and benchmarking methodology, and its application to SONOS, ReRAM, and ECRAM. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
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