Multilayer ferromagnetic spintronic devices for neuromorphic computing applications.

Based on ferromagnetic thin film systems, spintronic devices show substantial prospects for energy-efficient memory, logic, and unconventional computing paradigms. This paper presents a multilayer ferromagnetic spintronic device's experimental and micromagnetic simulation-based realization for neuromorphic computing applications. The device exhibits a temperature-dependent magnetic field and current-controlled multilevel resistance state switching. To study the scalability of the multilayer spintronic devices for neuromorphic applications, we further simulated the scaled version of the multilayer system read using the magnetic tunnel junction (MTJ) configuration down to 64 nm width. We show the device applications in hardware neural networks using the multiple resistance states as the synaptic weights. A varying pulse amplitude scheme is also proposed to improve the device's weight linearity. The simulated device shows an energy dissipation of 1.23 fJ for a complete potentiation/depression. The neural network based on these devices was trained and tested on the MNIST dataset using a supervised learning algorithm. When integrated as a weight into a 3-layer, fully connected neural network, these devices achieve recognition accuracy above 90% on the MNIST dataset. Thus, the proposed device demonstrates significant potential for neuromorphic computing applications.

Hardware implementation of memristor-based artificial neural networks.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.