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Defect-Tolerant Memristor Crossbar Circuits for Local Learning Neural Networks
Seokjin Oh1, Rina Yoon1, Kyeong-Sik Min1
1School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Nanomaterials (Basel, Switzerland)
|February 13, 2025
Summary
This study introduces a new time-multiplexing technique for Equilibrium Propagation (EP) using memristor circuits. This method improves defect tolerance in memristor-based neural network training, enhancing recognition rates.
Area of Science:
- Neuromorphic Engineering
- Computational Neuroscience
- Materials Science
Background:
- Local learning algorithms like Equilibrium Propagation (EP) offer energy-efficient alternatives to backpropagation for neural network training.
- Memristor-based circuits are promising for hardware implementation of EP but face challenges due to fabrication defects and variability.
- Existing EP implementations using memristors require separate circuits for free and nudge phases, leading to performance degradation.
Purpose of the Study:
- To propose a novel time-multiplexing technique for integrating EP's free and nudge phases into a single memristor circuit.
- To address the limitations of previous EP implementations caused by memristor defects and variability.
- To enhance the robustness and efficiency of memristor-based neural network training.
Main Methods:
- Developed a time-multiplexing scheme to combine the dynamic equations of EP's free and nudge phases within a single memristor circuit.
- Integrated defect and variability compensation mechanisms directly into the circuit design.
- Simulated the proposed circuit's performance using the MNIST dataset.
Main Results:
- The proposed time-multiplexing technique maintained a 92% recognition rate on the MNIST dataset, even with a 10% memristor defect rate.
- The previous implementation scheme showed a significant performance drop to 33% recognition rate under the same defect conditions.
- The novel circuit design resulted in reduced area overhead for both the EP solver and weight-update control circuits.
Conclusions:
- The proposed time-multiplexing technique effectively compensates for memristor defects and variability in EP-based neural networks.
- This approach offers a more robust and efficient solution for implementing local learning algorithms in hardware.
- The findings pave the way for more practical and scalable neuromorphic computing systems using memristor technology.
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