Memory devices and applications for in-memory computing

Traditional von Neumann computing systems involve separate processing and memory units. However, data
movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth
in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the
traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby
certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of
the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory
computing. In this Review, we provide a broad overview of the key computational primitives enabled by these
memory devices as well as their applications spanning scientific computing, signal processing, optimization,
machine learning, deep learning and stochastic computing.