Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userStochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference
104
0
0.0
(
0
)
Added by
Abhronil Sengupta
Publication date
2017
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
Spintronics, the use of spin of an electron instead of its charge, has received huge attention from research communities for different applications including memory, interconnects, logic implementation, neuromorphic computing, and many other applications. Here, in this paper, we review the works within spintronics, more specifically on spin-orbit torque (SOT) within different research groups. We also provide researchers an insight into the future potentials of the SOT-based designs. This comprehensive review paper covers different aspects of SOT-based design from device and circuit to architecture level as well as more ambitious and futuristic applications of such technology.
Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement Spike-Timing Dependent Plasticity. The proposed device offers the advantage of decoupled spike transmission and programming current paths, thereby leading to reliable operation during online learning. Possible arrangement of such devices in a crosspoint architecture can pave the way for ultra-dense neural networks. Simulation studies indicate that the device has the potential of achieving pico-Joule level energy consumption (maximum 2 pJ per synaptic event) which is comparable to the energy consumption for synaptic events in biological synapses.
Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computing-intensive task whose efficiency is physically limited by the bottlenecks of conventional computing platforms. In this work, a spintronics based stochastic computing approach is proposed for efficient Bayesian inference. The inherent stochastic switching behaviors of spintronic devices are exploited to build stochastic bitstream generator (SBG) for stochastic computing with hybrid CMOS/MTJ circuits design. Aiming to improve the inference efficiency, an SBG sharing strategy is leveraged to reduce the required SBG array scale by integrating a switch network between SBG array and stochastic computing logic. A device-to-architecture level framework is proposed to evaluate the performance of spintronics based Bayesian inference system (SPINBIS). Experimental results on data fusion applications have shown that SPINBIS could improve the energy efficiency about 12X than MTJ-based approach with 45% design area overhead and about 26X than FPGA-based approach.
Spin-orbit torques (SOTs), which rely on spin current generation from charge current in a nonmagnetic material, promise an energy-efficient scheme for manipulating magnetization in magnetic devices. A critical topic for spintronic devices using SOTs is to enhance the charge to spin conversion efficiency. Besides, the current-induced spin polarization is usually limited to in-plane, whereas out-of-plane spin polarization could be favored for efficient perpendicular magnetization switching. Recent advances in utilizing two important classes of van der Waals materials$-$topological insulators and transition-metal dichalcogenides$-$as spin sources to generate SOT shed light on addressing these challenges. Topological insulators such as bismuth selenide have shown a giant SOT efficiency, which is larger than those from three-dimensional heavy metals by at least one order of magnitude. Transition-metal dichalcogenides such as tungsten telluride have shown a current-induced out-of-plane spin polarization, which is allowed by the reduced symmetry. In this review, we use symmetry arguments to predict and analyze SOTs in van der Waal material-based heterostructures. We summarize the recent progress of SOT studies based on topological insulators and transition-metal dichalcogenides and show how these results are in line with the symmetry arguments. At last, we identify unsolved issues in the current studies and suggest three potential research directions in this field.
Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context of a temporal binding problem. We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced neural phase synchrony.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
