SPARQ: Spiking Early-Exit Neural Networks for Energy-Efficient Edge AI

ArXi:2603.14380v1 Announce Type: cross Spiking neural networks (SNNs) offer inherent energy efficiency due to their event-driven computation model, making them promising for edge AI deployment. However, their practical adoption is limited by the computational overhead of deep architectures and the absence of input-adaptive control. This work presents SPARQ, a unified framework that integrates spiking computation, quantization-aware