How does the accuracy and inference latency of lightweight BERT models compare to distilled versions of larger

Abstract

Abstract: The deployment of transformer-based models on resource-constrained edge devices represents a critical challenge in enabling real-time artificial intelligence applications. This comprehensive survey examines lightweight transformer architectures specifically designed for edge deployment, analyzing recent advances in model compression, quantization, pruning, and knowledge distillation techniques. We systematically review prominent lightweight variants including MobileBERT, TinyBERT, DistilBERT, EfficientFormer, EdgeFormer, and MobileViT, providing detailed performance benchmarks on standard data

Research Question

How does the accuracy and inference latency of lightweight BERT models compare to distilled versions of larger language models like RoBERTa or DeBERTa when deployed for real-time Android malware detection on edge devices?

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