In-Memory Computing: Memory Devices and Applications

Traditional 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 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. In this lecture, I will give an overview of the primary charge-based and resistance-based Seminar Titles (Maximum 3) memory devices being explored for in-memory computing as well as the key computational primitives they enable. Subsequently, I will present an overview of the key applications of in-memory computing that span scientific computing, signal processing, optimization, machine learning, deep learning, and stochastic computing.