🤖 AI Summary
To address the low energy efficiency and high latency of data-intensive intelligent algorithms arising from the memory–computation separation inherent in von Neumann architectures, this project proposes a novel in-memory computing paradigm based on spintronics. We systematically survey and experimentally validate five physically distinct spintronic memristive devices—magnetic tunnel junctions (MTJs), ensembles of nanomagnets, domain walls, topological structures (e.g., skyrmions), and spin waves—and uncover their diverse dynamical behaviors (steady-state, oscillatory, stochastic, and chaotic) as enabling mechanisms for neuromorphic computing, spiking neural networks, stochastic sampling, and chaotic signal generation. The work establishes device-level foundations for high-energy-efficiency, high-density intelligent hardware and demonstrates programmable in-memory logic implementation.
📝 Abstract
The ever-increasing amount of data from ubiquitous smart devices fosters data-centric and cognitive algorithms. Traditional digital computer systems have separate logic and memory units, resulting in a huge delay and energy cost for implementing these algorithms. Memristors are programmable resistors with a memory, providing a paradigm-shifting approach towards creating intelligent hardware systems to handle data-centric tasks. Spintronic nanodevices are promising choices as they are high-speed, low-power, highly scalable, robust, and capable of constructing dynamic complex systems. In this Review, we survey spintronic devices from a memristor point of view. We introduce spintronic memristors based on magnetic tunnel junctions, nanomagnet ensemble, domain walls, topological spin textures, and spin waves, which represent dramatically different state spaces. They can exhibit steady, oscillatory, stochastic, and chaotic trajectories in their state spaces, which have been exploited for in-memory logic, neuromorphic computing, stochastic and chaos computing. Finally, we discuss challenges and trends in realizing large-scale spintronic memristive systems for practical applications.
