Computing In-Memory, Revisited

The Von Neumann's architecture has been the dominant computing paradigm ever since its inception in the mid-forties. It revolves around the concept of a "stored program" in memory, and a central processing unit that executes the program. As an alternative, Processing-In-Memory (PIM) ideas have been around for at least two decades, however with very limited adoption. Today, three trends are creating a compelling motivation to take a second look. Novel devices such as memristor blur the boundary between memory and compute, effectively providing both in the same element. Power efficiency has become very important, both in the datacenter and at the edge. Machine learning applications driven by a data-flow model have become ubiquitous. In this paper, we sketch our Computing-In-Memory (CIM) vision, and its substantial performance and power improvement potential. Compared to PIM models, CIM more clearly separates computing from memory. We then discuss the programming model, which we consider the biggest challenge. We close by describing how CIM impacts non-functional characteristics, such as reliability, scale, and configurability.