Yong Shim

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets

Non-Boolean computing based on emerging postCMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by ~400× in comparison to a corresponding digital/ analog CMOS neuron implementation.

STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks

Recent years have witnessed growing interest in the use of artificial neural networks (ANNs) for vision, classification, and inference problems. An artificial neuron sums N weighted inputs and passes the result through a non-linear transfer function. Large-scale ANNs impose very high computing requirements for training and classification, leading to great interest in the use of post-CMOS devices to realize them in an energy efficient manner. In this paper, we propose a spin-transfer-torque (STT) device based on domain wall motion (DWM) magnetic strip that can efficiently implement a soft-limiting non-linear neuron (SNN) operating at ultra-low supply voltage and current. In contrast to previous spin-based neurons that can only realize hard-limiting transfer functions, the proposed STT-SNN displays a continuous resistance change with varying input current, and can therefore be employed to implement a soft-limiting neuron transfer function. Soft-limiting neurons are greatly preferred to hard-limiting ones due to their much improved modeling capacity, which leads to higher network accuracy and lower network complexity. We also present an ANN hardware design employing the proposed STT-SNNs and memristor crossbar arrays (MCA) as synapses. The ultra-low voltage operation of the magneto metallic STT-SNN enables the programmable MCA-synapses, computing analog-domain weighted summation of input voltages, to also operate at ultra-low voltage. We modeled the STT-SNN using micro-magnetic simulation and evaluated them using an ANN for character recognition. Comparisons with analog and digital CMOS neurons show that STT-SNNs can achieve around two orders of magnitude lower energy consumption.

Ising computation based combinatorial optimization using spin-Hall effect (SHE) induced stochastic magnetization reversal

Ising spin model is considered as an efficient computing method to solve combinatorial optimization problems based on its natural tendency of convergence towards low energy state. The underlying basic functions facilitating the Ising model can be categorized into two parts, “Annealing and Majority vote.” In this paper, we propose an Ising cell based on Spin Hall Effect (SHE) induced magnetization switching in a Magnetic Tunnel Junction (MTJ). The stochasticity of our proposed Ising cell based on SHE induced MTJ switching can implement the natural annealing process by preventing the system from being stuck in solutions with local minima. Further, by controlling the current through the Heavy-Metal (HM) underlying the MTJ, we can mimic the majority vote function which determines the next state of the individual spins. By solving coupled Landau-Lifshitz-Gilbert equations, we demonstrate that our Ising cell can be replicated to map certain combinatorial problems. We present results for two representative probl...

Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.

Low-power approximate convolution computing unit with domain-wall motion based "spin-memristor" for image processing applications

Convolution serves as the basic computational primitive for various associative computing tasks ranging from edge detection to image matching. CMOS implementation of such computations entails significant bottlenecks in area and energy consumption due to the large number of multiplication and addition operations involved. In this paper, we propose an ultra-low power and compact hybrid spintronic-CMOS design for the convolution computing unit. Low-voltage operation of domain-wall motion based magneto-metallic “Spin-Memristor”s interfaced with CMOS circuits is able to perform the convolution operation with reasonable accuracy. Simulation results of Gabor filtering for edge detection reveal ~ 2.5× lower energy consumption compared to a baseline 45nm-CMOS implementation.

Magnetoelectric oxide based stochastic spin device towards solving combinatorial optimization problems

Solving combinatorial optimization problems is challenging. Mapping onto the ground-state search problem of the Ising Hamiltonian is a promising approach in this field, where the components of the optimization set are modeled as artificial spin units. The search for a suitable physical system to realize these spin units is an active area of research. In this work, we have demonstrated a scheme to model the Ising Hamiltonian with multiferroic oxide/nanomagnet units. Although nanomagnet-based implementation has been shown before, we have utilized the magnetoelectric effect of the multiferroics to make voltagecontrolled spin units with less current flow in the network. Moreover, we have proposed a unique approach of configuring the coupling network of the system directly from the Ising Hamiltonian of a traveling salesman problem (TSP). We have developed a coupled micromagnetic simulation framework and solved TSPs of size 26-city and 15-city with an accuracy of 100% for the latter.

Coupled Spin-Torque Nano-Oscillator-Based Computation: A Simulation Study

In this article, we present a comprehensive study of four frequency locking mechanisms in Spin Torque Nano Oscillators (STNOs) and explore their suitability for a class of specialized computing applications. We implemented a physical STNO model based on Landau-Lifshitz-Gilbert-Slonczewski equation and benchmarked the model to experimental data. Based on our simulations, we provide an in-depth analysis of how the “self-organizing” ability of coupled STNO array can be effectively used for computations that are unsuitable or inefficient in the von-Neumann computing domain. As a case study, we demonstrate the computing ability of coupled STNOs with two applications: edge detection of an image and associative computing for image recognition. We provide an analysis of the scaling trends of STNOs and the effectiveness of different frequency locking mechanisms with scaling in the presence of thermal noise. We also provide an in-depth analysis of the effect of variations on the four locking mechanisms to find the most robust one in the presence of variations.

Computing with coupled Spin Torque Nano Oscillators

This paper gives an overview of coupled oscillators and how such oscillators can be efficiently used to perform computations that are unsuitable or inefficient in von-Neumann computing models. The “unconventional computing” ability of coupled oscillatory system is demonstrated through Spin Torque Nano Oscillators (STNOs). Recent experiments on STNOs have demonstrated their frequency of oscillation in few tens of gigahertz range, operating at low input currents. These attractive features and the ability to obtain frequency locking using a variety of techniques, make STNOs an attractive candidate for non-Boolean computing. We discuss coupled STNO systems for applications such as edge detection of an image, associative computing, determination of L2 norm for distance calculation, and pattern recognition.

Perspective: Stochastic magnetic devices for cognitive computing

Stochastic switching of nanomagnets can potentially enable probabilistic cognitive hardware consisting of noisy neural and synaptic components. Furthermore, computational paradigms inspired from the Ising computing model require stochasticity for achieving near-optimality in solutions to various types of combinatorial optimization problems such as the Graph Coloring Problem or the Travelling Salesman Problem. Achieving optimal solutions in such problems are computationally exhaustive and requires natural annealing to arrive at the near-optimal solutions. Stochastic switching of devices also finds use in applications involving Deep Belief Networks and Bayesian Inference. In this article, we provide a multi-disciplinary perspective across the stack of devices, circuits, and algorithms to illustrate how the stochastic switching dynamics of spintronic devices in the presence of thermal noise can provide a direct mapping to the computational units of such probabilistic intelligent systems.

Stochastic Switching of SHE-MTJ as a Natural Annealer for Efficient Combinatorial Optimization

Efficient computing models for combinatorial optimization problems, like the Ising spin model, have been researched intensively as an alternative to the von-Neumann based general-purpose computing. The bottleneck mainly stems from the fact that the computing complexity of such optimization problems increases exponentially with the size of the problem. Although efficient heuristic algorithms have been designed for such combinatorial problems, yet hardware implementations of such top-down approaches suffer from complex control requirements and frequent memory accesses. Interestingly, unique device characteristics of the recent emerging devices, such as stochastic spintronic devices, can potentially pave the way for efficient hardware implementation of such combinatorial optimization problems. In this work, we leverage stochastic switching of nano-magnets in presence of thermal noise to implement an efficient combinatorial optimization solver and demonstrate its feasibility by solving realistic NP-complete problems.