Coupled gradient-evolutionary learning in sparse memristive neuromorphic networks for robust edge intelligence.

Abstract

To address fragmented multimodal perception, high energy consumption in hardware deployment, and delayed responses in anomalous driving scenarios, this work proposes a coupled gradient-evolutionary learning framework deployed on sparse memristive neuromorphic networks for robust edge intelligence. The framework fuses physiological, visual, and auditory modalities and is validated across multimodal benchmarks on image, audio, and EEG tasks for real-time driving-safety monitoring, forming a closed-loop perception-analysis-decision pipeline that improves the reliability of safety-critical decisions. At the hardware level, neuromorphic processing units are constructed using two-dimensional material-based memristors. Leveraging in-memory computing and parallel processing capabilities of the memristive architecture, the proposed framework achieves energy-efficient classification of multimodal signals. At the algorithm level, a cross-species-inspired gradient-evolutionary architecture integrates local visual-cortex-inspired CNN learning for traffic-scene parsing with global Darwinian population evolution. Memristor write noise is utilized as a functional perturbation to drive the evolutionary training, which improves classification accuracy under quantization noise and enhances robustness against hardware non-idealities. With the synergy between evolutionary training and memristor-aware low-bit quantization, the framework exhibits enhanced natural sparsity and achieves tens-of-milliseconds inference latency, tens of milliwatts power consumption, and an energy cost of hundreds of microjoules per inference, resulting in about 5.8 ×  energy savings compared to conventional von Neumann edge computing architectures. Overall, this work provides a low-carbon, robust, and scalable edge intelligence solution for road safety decision-making in human-vehicle-environment systems, demonstrating the potential of neuromorphic computing for supporting carbon neutrality in transportation.