<![CDATA[Edge computing devices are increasingly tasked with performing artificial intelligence inference under strict constraints on processing capacity and power consumption. This study evaluates magnitude-based weight pruning and dynamic quantization as practical model compression techniques for energy-efficient edge AI deployment. MobileNetV2, pretrained on ImageNet, was adapted to the CIFAR-10 classification task and compressed under three configurations: 40% L1 unstructured pruning followed by recovery fine-tuning (Prune40), dynamic INT8 post-training quantization (QuantINT8), and a sequential combination of both (Prune+Quant). All experiments were executed on a physical Intel N150 mini PC with a thermal design power of 6 watts, using PyTorch 2.1 in CPU-only inference mode. Results show that Prune40 reduced inference latency by 17.9% while simultaneously improving classification accuracy by 1.04 percentage points, attributed to the implicit regularisation effect of sparse weight removal and recovery fine-tuning. QuantINT8 yielded moderate latency savings (6.6%) with negligible accuracy loss. The combined pipeline achieved the lowest absolute latency at a marginal energy overhead. These findings establish magnitude pruning with recovery training as the most effective single-step compression strategy for low-power x86 edge platforms.]]>
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