E2AM: A Controlled Evaluation Framework for Energy-Aware Training and Deployment of Deep Vision Models

Abstract

The increasing energy cost of training deep vision models has made computational efficiency an important objective in modern deep learning. Although tensor caching, automatic mixed precision (AMP), gradient accumulation, $\ell _{1}$ sparsity regularization, learning-rate scheduling, pruning, and quantization are established techniques, their individual and cumulative accuracy–energy effects are difficult to interpret when several factors are modified simultaneously. This paper presents E2AM, a controlled, hardware-measured framework for evaluating five training-time interventions—tensor caching, AMP, gradient accumulation, $\ell _{1}$ sparsity regularization, and cosine learning-rate scheduling—together with an energy-aware early-stopping rule. The interventions are evaluated individually and cumulatively across ResNet-50, EfficientNetV2-S, MobileViTv2, and ConvNeXtV2-Tiny using CIFAR-10, CIFAR-100, and Tiny-ImageNet. All models are trained from random initialization under a common maximum budget of 50 epochs, and GPU-board energy is integrated from hardware power telemetry over 180 logged runs. Relative to the all-off reference configuration, the complete E2AM configuration reduces measured training energy by 30–63% across the evaluated architecture–dataset pairs and achieves higher observed best validation accuracy for 11 of the 12 pairs within the fixed training budget. ConvNeXtV2-Tiny on CIFAR-10 provides a lower-energy near-iso-accuracy operating point, with an accuracy difference of −0.08 percentage points. Because each configuration is evaluated using a single seed, the reported accuracy differences are interpreted as descriptive operating-point comparisons rather than inferential estimates. The observed responses also differ between the SGD-trained architectures and the AdamW-trained ConvNeXtV2-Tiny configuration. Because optimizer and architecture are not independently varied, this behavior is interpreted as architecture–optimizer-configuration dependence rather than as an isolated causal optimizer effect. The deployment analysis further reports post-optimization accuracy, serialized artifact size, latency, and throughput. INT8 quantization provides an approximately fourfold reduction in serialized artifact size for most graph-traceable configurations, whereas one configuration exhibits substantial accuracy degradation. Therefore, E2AM contributes a controlled evaluation methodology for characterizing training and deployment efficiency effects rather than proposing novel constituent optimization techniques.