Towards an Energy-Efficient Anomaly Detector: When Multimodal Anomaly Detection Meets Spiking Neural Network

Recent advances in multimodal anomaly detection (MAD) primarily rely on more sophisticated and powerful artificial neural networks (ANNs) to improve detection performance. However, less attention has been paid to the fact that ANN-based paradigms often require high energy consumption to achieve these performance gains. This gap raises a key challenge: Can we significantly reduce energy consumption while maintaining competitive performance? Spiking neural networks (SNNs), which employ binary spikes and event-driven computation, have emerged as a promising energy-efficient alternative to conventional ANNs. Motivated by this, we propose SpikingMAD, a spike-driven MAD framework designed to achieve both low power consumption and high performance. Specifically, we develop a spike-driven multimodal reconstruction network that restores abnormal features to normal ones through spike-sparse computation and multiplication-free operations. Furthermore, given that dot-product attention fails to measure the similarity between RGB and depth features encoded by spiking neurons, we introduce a spike-driven Hamming cross-attention mechanism to capture cross-modal feature similarity and facilitate multimodal feature fusion. Extensive experiments on MVTec 3D-AD, Real-IAD D3, and Eyecandies demonstrate that SpikingMAD achieves competitive performance in terms of accuracy, memory usage, frame rate, and energy consumption, while also showing strong potential in few-shot scenarios. Code: https://anonymous.4open.science/r/SpikingMAD.