Ecoefficiency Analysis and Regression in Data Conversion for Spiking Neural Network Training

Although the spotlight on ecological efficiency in artificial intelligence (AI) research is intensifying, its integration remains underdeveloped. Addressing this gap is essential to align algorithmic performance with ecological responsibility and support the carbon neutrality targets of the Organization for Economic Cooperation and Development (OECD). This study proposes a methodology to measure CO ₂ emissions and energy consumption during dataset encoding and training of Spiking Neural Networks (SNNs) in regression tasks. Using rate encoding with varied hyperparameters, we compare the original PilotNet convolutional neural network (CNN) with its SNN adaptation across three autonomous driving datasets: Udacity, Sully Chen (Palo Alto), and AirSim. Results show that temporal depth ( S , number of time steps) is the primary driver of environmental footprint, with increases from S  = 5 to S  = 25 raising emissions without proportional accuracy gains, while the gain parameter ( G , scaling factor for spike generation probability) has negligible impact. On Udacity, CNN outperforms SNNs in both accuracy and footprint. On Palo Alto, an SNN slightly improves accuracy but at significantly higher emissions. On AirSim, SNNs outperform CNN in accuracy but incur substantially greater environmental cost. These findings indicate that, for rate‐encoded SNNs of this architecture executed on conventional CPU/GPU hardware, the accuracy‐energy trade‐off varies across driving environments and that ecoefficiency gains remain limited, requiring neuromorphic platforms and improved metrics capturing both performance and environmental impact.