Hybrid Precision Gradient Accumulation for CNN-LSTM in Sports Venue Buildings Analytics: Energy-Efficient Spatiotemporal Modeling

Abstract

We propose a hybrid CNN-LSTM architecture for energy-efficient spatiotemporal modeling in sports venue analytics, addressing the dual challenges of computational efficiency and prediction accuracy in dynamic environments. The proposed method integrates layered mixed-precision training with gradient accumulation, dynamically allocating bitwidths across the spatial (CNN) and temporal (LSTM) layers while maintaining robustness through a computational memory unit. The CNN feature extractor employs higher precision for early layers to preserve spatial details, whereas the LSTM reduces the precision for temporal sequences, optimizing energy consumption under a hardware-aware constraint. Furthermore, the gradient accumulation over micro-batches simulates large-batch training without memory overhead, and the computational memory unit mitigates precision loss by storing the intermediate gradients in high-precision buffers before quantization. The system is realized as a ResNet-18 variant with mixed-precision convolutions and a two-layer bidirectional LSTM, deployed on edge devices for real-time processing with sub 5 ms latency. Our theoretical analysis predicts a 35–45% energy reduction versus fixed-precision models while maintaining <2% accuracy degradation, crucial for large-scale deployment. The experimental results demonstrate a 40% reduction in energy consumption compared to fixed-precision models while achieving over 95% prediction accuracy in tasks such as occupancy forecasting and HVAC control. This work bridges the gap between energy efficiency and model performance, offering a scalable solution for large-scale venue analytics.