Pruner: An Efficient Cross-Platform Tensor Compiler with Dual Awareness

Tensor program optimization on Deep Learning Accelerators (DLAs) is critical for efficient model deployment. Although search-based Deep Learning Compilers (DLCs) have achieved significant performance gains compared to manual methods, they still suffer from the persistent challenges of low search efficiency and poor cross-platform adaptability. In this paper, we propose $\textbf{Pruner}$, following hardware/software co-design principles to hierarchically boost tensor program optimization. Pruner comprises two primary components: a Parameterized Static Analyzer ($\textbf{PSA}$) and a Pattern-aware Cost Model ($\textbf{PaCM}$). The former serves as a hardware-aware and formulaic performance analysis tool, guiding the pruning of the search space, while the latter enables the performance prediction of tensor programs according to the critical data-flow patterns. Furthermore, to ensure effective cross-platform adaptation, we design a Momentum Transfer Learning ($\textbf{MTL}$) strategy using a Siamese network, which establishes a bidirectional feedback mechanism to improve the robustness of the pre-trained cost model. The extensive experimental results demonstrate the effectiveness and advancement of the proposed Pruner in various tensor program tuning tasks across both online and offline scenarios, with low resource overhead. The code is available at https://github.com/qiaolian9/Pruner.

H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

Recently, deep learning methods have been widely used for tumor segmentation of multimodal medical images with promising results. However, most existing methods are limited by insufficient representational ability, specific modality number and high computational complexity. In this paper, we propose a hybrid densely connected network for tumor segmentation, named H-DenseFormer, which combines the representational power of the Convolutional Neural Network (CNN) and the Transformer structures. Specifically, H-DenseFormer integrates a Transformer-based Multi-path Parallel Embedding (MPE) module that can take an arbitrary number of modalities as input to extract the fusion features from different modalities. Then, the multimodal fusion features are delivered to different levels of the encoder to enhance multimodal learning representation. Besides, we design a lightweight Densely Connected Transformer (DCT) block to replace the standard Transformer block, thus significantly reducing computational complexity. We conduct extensive experiments on two public multimodal datasets, HECKTOR21 and PI-CAI22. The experimental results show that our proposed method outperforms the existing state-of-the-art methods while having lower computational complexity. The source code is available at https://github.com/shijun18/H-DenseFormer.