Natural language (NL)-driven table discovery identifies relevant tables from large table repositories based on NL queries. While current deep-learning-based methods using the traditional dense vector search pipeline, i.e., representation-index-search , achieve remarkable accuracy, they face several limitations that impede further performance improvements: (i) the errors accumulated during the table representation and indexing phases affect the subsequent search accuracy; and (ii) insufficient query-table interaction hinders effective semantic alignment, impeding accuracy improvements. In this paper, we propose a novel framework Birdie, using a differentiate search index. It unifies the indexing and search into a single encoder-decoder language model, thus getting rid of error accumulations. Birdie first assigns each table a prefix-aware identifier and leverages a large language model-based query generator to create synthetic queries for each table. It then encodes the mapping between synthetic queries/tables and their corresponding table identifiers into the parameters of an encoder-decoder language model, enabling deep query-table interactions. During search, the trained model directly generates table identifiers for a given query. To accommodate the continual indexing of dynamic tables, we introduce an index update strategy via parameter isolation, which mitigates the issue of catastrophic forgetting. Extensive experiments demonstrate that Birdie outperforms state-of-the-art dense methods by 16.8% in accuracy, and reduces forgetting by over 90% compared to other continual learning approaches.

In recent years, with the gradual slowing of Moore’s Law and the development of deep learning , the demand for hardware performance of executing deep learning based applications has significantly increased. In this case, deep learning compilers have been proven to maximize hardware performance while keeping computational power constant, especially the end-to-end compiler Tensor Virtual Machine (TVM). TVM optimizes tensors by finding excellent parallel computing schemes, thereby achieving the goal of improving the performance of neural network inference. However, there is still untapped potential in current optimization methods. However, existing optimization methods based on the TVM, such as Genetic Algorithms Tuner (GA-Tuner), have failed to achieve a balance between optimization performance and optimization time. The intolerable duration of optimization detracts from TVM’s usability, rendering it challenging to extend into the scientific community. This paper introduces a novel deep learning compilation optimization framework base on TVM called Agile Optimization Framework (AOF), which incorporates a tuner based on the latest Beluga Whale Optimization Algorithm (BWO). The BWO is adept at tackling complex problems characterized by numerous local optima, making it particularly suitable for hardware compilation optimization scenarios. We further propose an Evolving Epsilon Strategy (EES), a search strategy that adaptively adjusts the balance between exploration and exploitation, thereby enhancing the effectiveness of the algorithm. Additionally, we developed a supervised Tuning Accelerator (TA) aimed at reducing the time required for optimization and enhancing efficiency. Comparative experiments demonstrate that AOF achieves 11.36%–66.20% improvement in performance and 30.30%–54.60% reduction in optimization time, significantly outperforming the control group.