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Ferrimagnet-Based Neuromorphic Device Mimicking the Ventral Visual Pathway for High-Accuracy Target Recognition
Junwei Zeng1,2, Yabo Chen1,2, Jiahao Liu3,4
1The Key Laboratory of Advanced Microprocessor Chips and Systems, College of Computer, National University of Defense Technology, Changsha 410073, China.
ACS Applied Materials & Interfaces
|October 21, 2024
Summary
Researchers developed a novel ferrimagnetic neuron and artificial synapse for spintronic neural networks. This enables deeper, more interpretable artificial intelligence to mimic the human brain's visual recognition capabilities.
Area of Science:
- Neuroscience
- Materials Science
- Computer Science
Background:
- The ventral visual pathway (VVP) in the human brain excels at target recognition through deep hierarchical processing.
- Spintronic artificial neural networks (ANNs) show promise for target recognition but lack interpretability and network depth, limiting their ability to mimic the VVP.
- Existing spintronic ANNs struggle to replicate the biological realism and depth required for VVP hardware implementation.
Purpose of the Study:
- To develop a biorealistic spintronic device and interpretable deep network structure capable of mimicking the human VVP.
- To introduce a novel ferrimagnetic neuron with a continuously differentiable exponential linear unit (CeLu) activation function.
- To create artificial synapses with high linearity and symmetry using ferrimagnetic materials.
Main Methods:
- Designed and fabricated a ferrimagnetic neuron incorporating a CeLu activation function, enhancing biological realism and mitigating depth limitations.
- Developed artificial synapses from ferrimagnetic materials, demonstrating high linearity and symmetry suitable for weight update algorithms.
- Proposed and simulated an all-spin convolutional neural network (CNN) architecture utilizing these novel neurons and synapses to mimic the VVP.
Main Results:
- The proposed all-spin CNN achieved high recognition accuracies: over 91% on CIFAR-10 and 98% on MNIST datasets.
- Demonstrated significant performance improvements of 1.13% and 1.76% compared to state-of-the-art spintronic neuromorphic models.
- The bionic CNN, using experimentally derived parameters, showcased enhanced interpretability and network depth.
Conclusions:
- The developed ferrimagnetic neuron and synapse offer a promising approach for creating interpretable and deep spintronic neural networks.
- This work presents a viable method for improving the bionic performance of spintronic device-based neuromorphic computing.
- The proposed all-spin CNN architecture effectively mimics the human VVP, paving the way for advanced artificial intelligence hardware.
