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A Parameter-Agnostic Adaptive Compensation in Memristor-Based Neuromorphic Systems for Parasitic Resistance
Texu Liu1, Hanbo Ren1, Peiwen Tong1
1College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.
Micromachines
|May 4, 2026
Summary
We developed a Parameter-Agnostic Adaptive Compensation (PAAC) method to address parasitic resistance issues in memristor-based neuromorphic computing. This approach significantly improves scalability and reliability for efficient in-memory processing.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Computer Science
Background:
- Memristor-based neuromorphic computing enables efficient in-memory processing.
- Parasitic resistances in crossbar arrays cause IR-drop, hindering scalability and reliability during vector-matrix multiplication (VMM).
- Existing solutions face high latency or require impractical parameter extraction.
Purpose of the Study:
- To propose a novel method overcoming parasitic resistance limitations in memristor crossbar arrays.
- To enable scalable and reliable neuromorphic hardware without precise parameter knowledge.
- To reduce computational complexity and improve VMM accuracy.
Main Methods:
- Developed a Parameter-Agnostic Adaptive Compensation (PAAC) method based on a distributed linear approximation model.
- Derived a simplified linear relationship independent of specific resistance values.
- Implemented a single-step pre-calibration for a global compensation factor.
Main Results:
- Reduced current distortion from 71% to 2% in hardware experiments.
- Restored classification accuracy from 89% to 95% in large-scale simulations.
- Achieved ultra-low computational complexity during inference.
Conclusions:
- The PAAC method offers a robust, low-overhead solution for neuromorphic hardware.
- Eliminates dependency on precise parameter modeling, facilitating large-scale integration.
- Enables high-precision, scalable neuromorphic systems.
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