Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding
Steven Peters
Recognizing traffic signs is a fundamental perception task for automated driving systems and requires high accuracy under strict latency and energy constraints. Convolutional neural networks (CNNs) achieve strong performance but can be computationally demanding for embedded platforms. Spiking convolutional neural networks (SCNNs) offer an event-driven alternative that can reduce computation through sparse activity, yet their accuracy often degrades under very low-latency settings with few time s