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

TL;DR

SPARQ introduces a unified framework for spiking neural networks that combines quantization, reinforcement learning, and early exits, significantly improving energy efficiency and adaptability for edge AI applications.

Contribution

The paper presents SPARQ, a novel framework integrating spiking computation, quantization-aware training, and reinforcement learning-guided early exits for more efficient and adaptive edge AI inference.

Findings

Up to 5.15% higher accuracy than QSNNs.

Over 330 times lower energy consumption compared to baseline SNNs.

More than 90% reduction in synaptic operations across datasets.

Abstract

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 training, and reinforcement learning-guided early exits for efficient and adaptive inference. Evaluations across MLP, LeNet, and AlexNet architectures demonstrated that the proposed Quantised Dynamic SNNs (QDSNN) consistently outperform conventional SNNs and QSNNs, achieving up to 5.15% higher accuracy over QSNNs, over 330 times lower system energy compared to baseline SNNs, and over 90 percent fewer synaptic operations across different datasets. These results validate SPARQ as a hardware-friendly, energy-efficient solution for real-time AI at the edge.