Among many proposed new device concepts and architectural designs, there is increasing interest in a fundamentally different form of brain-like logic based on probabilistic inference that is far more effective and energy efficient in dealing with the problems of search and recognition posed by the ever-increasing amounts and demands of "big data." The Boltzmann Machine (BM) proposed by Ackley, Hinton, and Sejnowski is a type of parallel constraint satisfaction network capable of learning the underlying constraints that characterize a domain through examples from the domain. Herein we propose a stochastic nanomagnet based hardware unit, which we call a "p-bit", as a primitive BM computing element. Our unique p-bit, simply consisting of an input and a low energy barrier (low kBT) nanomagnet, has binary states and adopts these states as a probabilistic function of its input states and their weights. This elegant, simple hardware form of a random number generator is the key element to distinguish our proposal from other hardware implementations of BMs and will be the focus of the center’s study on Probabilistic Spin Logic Devices (PSL).