Large-Margin Dynamic Memory Refresh for Persistent In-Memory Computing and Learning

Abstract

In-memory-computing promises to reduce power consumption by tightly integrating analog compute logic and memory, thus avoiding costly data transfers. This requires analog storage with high density, sufficient precision and long retention. However, increasing retention at the device level often comes at the expense of excessive area, write energy and/or precision. We instead propose an algorithmic solution to realize dynamic semi-volatile memory, rooted in large-margin statistical learning theory, that dynamically refreshes the analog vector contents of an in-memory computing accelerator towards one among a large discrete set of stable states, and can thus extend its retention time indefinitely. We present three different variants of this algorithm, analyze its capabilities and limitations, and discuss potential applications.