Do Newer Lightweight CNNs Perform Better Under Resource Constraints? A Controlled Multigenerational Study of Architecture, Initialization, Training Budget, and Efficiency
arXiv:2607.01984v1 Announce Type: new Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims require controlled evaluation. This study compares nine lightweight CNN model packages across CIFAR-10, CIFAR-100, and Tiny ImageNet under a shared downstream protocol. We report top-1 accuracy, macro F1, top-5 accuracy, parameter count, FP32 storage, GMACs, batch-size-1 latency on an NVIDIA L4 and AMD Ryzen 5 5500U CPU, peak PyTorch CUDA allocated tensor memory, and point estimate Pareto frontiers. Ef
The proliferation of AI models demands increasingly efficient architectures, making controlled studies on lightweight CNN performance under various constraints crucial for current development cycles.
This study provides critical data for optimizing AI deployment, especially on resource-constrained devices, directly impacting the economic viability and accessibility of AI models.
The empirical comparison of lightweight CNNs offers a clearer understanding of their true performance trade-offs, guiding hardware and software co-design decisions and potentially altering model selection strategies.
- · Edge AI providers
- · Hardware manufacturers (NVIDIA, AMD)
- · AI developers focused on efficiency
- · Applications requiring low-resource AI
- · Developers solely relying on large, inefficient models
- · Cloud providers if more workloads shift to edge devices (long-term)
The study directly informs the selection and optimization of lightweight CNNs for various deployment scenarios.
Improved efficiency could accelerate the adoption of AI in embedded systems and significantly lower the cost of inference, broadening AI's application scope.
Generalized hardware-aware AI model design principles may emerge, leading to an 'efficiency-first' paradigm shift across the entire AI development lifecycle.
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Read at arXiv cs.LG