E2AM: A Controlled Evaluation Framework for Energy-Aware Training and Deployment of Deep Vision Models
Year: 2026
Type: article
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.
Source: IEEE Access
Institution: VIT-AP University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W7204147172