One of the most promising emerging non-volatile memories is magnetic tunnel junction (MTJ); this is a Spintronic element where electronic charge and spin are both used for storing and manipulating digital information. Indeed, many companies have proposed this as a universal memory targeting embedded RAM, DRAM, and storage class memory domains primarily due to features like non-volatility, ultra-low power consumption, high endurance, rad-hardness, etc. However, the increasingly popular data-centric approach for solving non-Boolean problems, often called in memory computing, may also benefit by exploiting this kind of device.
Non-Boolean Computing with Spintronic Devices explores the latest research areas that employ spintronic devices for non-Boolean computing purposes. Due to the physical limits of traditional computing frameworks, researchers have focused on unconventional solving paradigms like neural networks, associative memory, neuromorphic computing, etc. This monograph also illustrates a novel mechanism to solve computationally expensive binary quadratic optimization problems via an energy minimization framework of nanomagnets. This hardware platform opens the possibility of achieving energy efficient processors such as the Ising model and Bayesian inference co-processor. However, the technology readiness level of spintronic devices is still maturing, so the research on the computing frameworks based on these devices is not static, rather dynamic. This monograph surveys the research to date and is an ideal reference for anyone interested in how the field is developing.