Dynamical Neural Network Based on Spin Transfer Nano-Oscillators

被引:5
|
作者
Rodrigues, Davi R. [1 ]
Raimondo, Eleonora [2 ]
Puliafito, Vito [1 ]
Moukhadder, Rayan [1 ]
Azzerboni, Bruno [3 ]
Hamadeh, Abbass [4 ,5 ]
Pirro, Philipp [4 ,5 ]
Carpentieri, Mario [1 ]
Finocchio, Giovanni [2 ]
机构
[1] Politecn Bari, Dept Elect & Informat Engn, I-70126 Bari, Italy
[2] Univ Messina, Dept Math & Comp Sci, Phys Sci & Earth Sci, I-98166 Messina, Italy
[3] Univ Messina, Dept Engn, I-98166 Messina, Italy
[4] Tech Univ Kaiserslautern, D-67663 Kaiserslautern, Germany
[5] Landesforschungszentrum OPTIMAS, D-67663 Kaiserslautern, Germany
关键词
Biological neural networks; Magnetization; Neurons; Behavioral sciences; Task analysis; Performance evaluation; Training; Dynamical neurons; spin-transfer-nano oscillators; spintronics; time nonlocality;
D O I
10.1109/TNANO.2023.3330535
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Spintronic technology promises to significantly increase the efficiency and scalability of neural networks by employing optimized task-oriented device components that exhibit intrinsic nonlinearity, temporal nonlocality, scalability, and electrical tunability. In particular, the functional response of spin-transfer torque oscillators can be designed to naturally emulate the building blocks of neural networks, such as short-term memory, hierarchy, and nonlinearity. We propose spin-transfer nano-oscillators as a dynamic neuron that can be used in a neural network coupled with a fully connected layer to perform classification tasks. In this concept, successive nodes of the neural network correspond to successive time steps, so that the nonlinearity and memory of the system can be naturally exploited. The tunability of the device allows to project initial configurations in well-defined regions of the phase space where classification is easily performed. Furthermore, training is performed using optimal control theory. We emphasize that the devices benefit from more realistic models compared to simpler analytical models and is robust against device-to-device variations. We tested the performance of the network on two types of datasets and obtained 99% accuracy. Although these systems are computationally expensive, their hardware implementation is simple and inexpensive.
引用
收藏
页码:800 / 805
页数:6
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