A Machine Learning Framework for Defect-Aware Neuromorphic Circuit Inference Error Correction

Project Abstract

Inference-stage error correction routines designed for hardware-level defects in analog neuromorphic processor layouts.

Designed to address domain scalability challenges, this engineering prototype validates predictive modeling and computational analysis. It provides clean deployment scripts, structured models, and evaluation routines suitable for academic and industrial research labs.

Core System Features

• Optimized Modeling Pipeline: Uses state-of-the-art architectures to achieve stable convergence and high-accuracy scoring.
• Defect-Aware & Domain Adaptation Controls: Incorporates error correction routines and adaptive filtering layers to boost cross-system generalizability.
• Practical Implementation: Developed as a model for university lab cell testing and local prototyping trials.