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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08469 |
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| _version_ | 1866917761880424448 |
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| author | Gupta, Saumya Vadde, Venkatesh Muralidharan, Bhaskaran Sharma, Abhishek |
| author_facet | Gupta, Saumya Vadde, Venkatesh Muralidharan, Bhaskaran Sharma, Abhishek |
| contents | Spintronic-based neuromorphic hardware offers high-density and rapid data processing at nanoscale lengths by leveraging magnetic configurations like skyrmion and domain walls. Here, we present the maximal hardware implementation of a convolutional neural network (CNN) based on a compact multi-bit skyrmion-based synapse and a hybrid CMOS domain wall-based circuit for activation and max-pooling functionalities. We demonstrate the micromagnetic design and operation of a circular bilayer skyrmion system mimicking a scalable artificial synapse, demonstrated up to 6-bit (64 states) with an ultra-low energy consumption of 0.87 fJ per state update. We further show that the synaptic weight modulation is achieved by the perpendicular current interaction with the labyrinth-maze like uniaxial anisotropy profile, inducing skyrmionic gyration, thereby enabling long-term potentiation (LTP) and long-term depression (LTD) operations. Furthermore, we present a simultaneous rectified linear (ReLU) activation and max pooling circuitry featuring a SOT-based domain wall ReLU with a power consumption of 4.73 $μ$W. The ReLU function, stabilized by a parabolic uniaxial anisotropy profile, encodes domain wall positions into continuous resistance states coupled with the HSPICE circuit simulator. Our integrated skyrmion and domain wall-based spintronic hardware achieves 98.07% accuracy in convolutional neural network (CNN) based pattern recognition task, consuming 110 mW per image. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Convolutional Neural Network Architecture Design using Magnetic Skyrmion and Domain Wall Gupta, Saumya Vadde, Venkatesh Muralidharan, Bhaskaran Sharma, Abhishek Mesoscale and Nanoscale Physics Spintronic-based neuromorphic hardware offers high-density and rapid data processing at nanoscale lengths by leveraging magnetic configurations like skyrmion and domain walls. Here, we present the maximal hardware implementation of a convolutional neural network (CNN) based on a compact multi-bit skyrmion-based synapse and a hybrid CMOS domain wall-based circuit for activation and max-pooling functionalities. We demonstrate the micromagnetic design and operation of a circular bilayer skyrmion system mimicking a scalable artificial synapse, demonstrated up to 6-bit (64 states) with an ultra-low energy consumption of 0.87 fJ per state update. We further show that the synaptic weight modulation is achieved by the perpendicular current interaction with the labyrinth-maze like uniaxial anisotropy profile, inducing skyrmionic gyration, thereby enabling long-term potentiation (LTP) and long-term depression (LTD) operations. Furthermore, we present a simultaneous rectified linear (ReLU) activation and max pooling circuitry featuring a SOT-based domain wall ReLU with a power consumption of 4.73 $μ$W. The ReLU function, stabilized by a parabolic uniaxial anisotropy profile, encodes domain wall positions into continuous resistance states coupled with the HSPICE circuit simulator. Our integrated skyrmion and domain wall-based spintronic hardware achieves 98.07% accuracy in convolutional neural network (CNN) based pattern recognition task, consuming 110 mW per image. |
| title | A Comprehensive Convolutional Neural Network Architecture Design using Magnetic Skyrmion and Domain Wall |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2407.08469 |