Large language models (LLMs) have revolutionized natural language processing (NLP) tasks by achieving state-of-the-art performance across a range of benchmarks. Central to the success of these models is the integration of sophisticated architectural components aimed at improving training stability, convergence speed, and generalization capabilities. Among these components, normalization operation, such as layer normalization (LayerNorm), emerges as a pivotal technique, offering substantial benefits to the overall model performance. However, previous studies have indicated that normalization operations can substantially elevate processing latency and energy usage. In this work, we adopt the principles of algorithm and hardware co-design, introducing a holistic normalization accelerating method named HAAN. The evaluation results demonstrate that HAAN can achieve significantly better hardware performance compared to state-of-the-art solutions.

With the reducing size of fingerprint collection modules in mobile devices, partial fingerprints are increasingly characterized by smaller overlapping areas and higher self-similarity. Existing methods either aggregate similarity scores from individual Single-to-Single recognition or directly employ a Single-to-Multiple network to verify the match between the query and templates. However, these methods either lack sufficient interaction between templates, or fail to provide adequate supervision for the alignment process, a crucial step in fingerprint recognition, thereby limiting overall accuracy. In this paper, we propose a novel partial fingerprint recognition strategy termed Calibrated Single-to-Multiple (CaS2M), which first calibrates template fingerprints individually, then combines them with the query fingerprint in a matcher network for feature fusion. Building upon this strategy, we develop a dual-stage framework tailored to real-world applications. During enrollment, a lightweight patch-based feature indexing algorithm and a template selection strategy are employed accounting for limited hardware resources. For authentication, independent calibration is first applied, followed by an attention-based matcher network to verify identity consistency. Experimental results on multiple public datasets (NIST 302, NIST SD4, SpoofGAN, FVC2002 DB1A & DB3A) and a self-build dataset demonstrate that our framework achieves superior performance over state-of-the-art algorithms, providing new insights for multi-template partial fingerprint recognition.