In-Memory-Computing for Machine Cognition at the Edge

In-Memory-Computing (IMC) is an alternative computer architecture to the standard von Neumann machine, in which compute functionality is incorporated with the state holding elements - memory. This idea is inspired from biology, where memory and processing are integrated in a single structure, the synapse. In this paper we explore the sometimes counterintuitive implications of integrating processing within the memory cells demonstrate how the latter can be leveraged to dramatically increase energy efficiency of computing hardware for machine cognition. We demonstrate the performance in real-world application examples on actual scaled up physical hardware (a 79 core IMC System On Chip (SOC). We finally show how recent developments allow IMC computers (as opposed to von Neumann computers with IMC accelerators) have the weights storage density necessary for embodied intelligence and machine cognition at the edge.