A Parameter-Agnostic Adaptive Compensation in Memristor-Based Neuromorphic Systems for Parasitic Resistance.

Memristor-based neuromorphic computing offers a promising pathway for efficient in-memory processing. However, the scalability and reliability of such systems are severely compromised by parasitic resistances (including line and input resistances) in crossbar arrays, which cause significant IR-drop during vector-matrix multiplication (VMM). Existing research often suffers from high computational latency or relies on the precise extraction of parasitic parameters, which is impractical and computationally expensive for large-scale integration. To overcome these limitations, we propose a Parameter-Agnostic Adaptive Compensation (PAAC) method based on a distributed linear approximation model. By analyzing the circuit characteristics, we conquered the challenge of coupling between parasitic effects and output current, deriving a simplified linear relationship that requires no prior knowledge of specific resistance values. The PAAC method involves only a single-step pre-calibration experiment to determine a global compensation factor, achieving an ultra-low computational complexity during inference. We validated the method using a comprehensive two-stage strategy: board-level hardware experiments confirmed its feasibility by reducing current distortion from 71% to 2%, while extensive large-scale HSPICE simulations verified its scalability, restoring classification accuracy from 89% to 95%. This work provides a robust, low-overhead solution that eliminates the dependency on precise parameter modeling, facilitating the realization of large-scale, high-precision neuromorphic hardware.