# Cross-Feature Transfer Learning for Efficient Tensor Program Generation
## 3 Design and Implementation
### End-to-End Execution Steps of the Framework  
Figure 1 presents a comprehensive overview of our framework's end-to-end workflow. Our approach is based on TVM v0.8 dev0, serving as the foundational platform for conducting heterogeneous transfer learning. The framework is designed to facilitate the seamless transition of computational models between different hardware environments. In this process, the user interacts with the framework via the TVM APIs, engaging in a series of key steps, as illustrated by the circled numbers:  
1. The user initiates the workflow by providing the computational framework in a supported format, such as TensorFlow, ONNX [44], or PyTorch. 2. The framework undertakes high-level optimization and subgraph partitioning, resulting in the generation of smaller subgraphs. These subgraphs constitute the candidatesfor the search space for subsequent feature extraction operations. Measurements are performed on these subgraphs for a particular hardware configuration. The outcomes are subsequently stored in individual JSON [45] files for each subgraph, encompassing schedule primitive hints and the execution time for each auto-generated tensor program. 3. Domain-specific information, encompassing details like kernel dimensions and tensor operations, is preserved from the subgraphs, forming a distinctive feature set. 4. For each data-point entry or kernel, critical hardware information is meticulously recorded, including hardware architecture, maximum thread count, register allocation, and threads per block. This data constitutes the static hardware dataset. 5. A probabilistic and exploratory study is conducted on this feature set to identify features of significant importance. This hardware characterization plays a pivotal role in mapping features from the source hardware to the target hardware, ensuring compatibility. The autotuner is trained using this dataset, extending the principles of a one-shot tuner [10]. This training equips the autotuner with the capability to generate tensor programs on the target device automatically, with or without retraining. 6. For users seeking fine-tuning capabilities, the framework offers the option to fine-tune the autotuner using online hardware features. Experimental work includes selective task retraining and a methodology inspired by the Lottery Ticket Hypothesis-based technique [20]. Our approach reduces retraining time and minimizes the dataset size required for fine-tuning. The utilization of attention heads supports memory-augmented fine-tuning via bidirectional LSTM [46]. Given the framework's attention-based training strategy, the autotuner model undergoes a comprehensive training phase only once, mitigating the necessity for extensive data on the target device for full retraining. 7. Following the execution of steps 5 and 6, a set of top-k tensor programs is generated. To facilitate user selection based on specified metrics, we employ ranking loss. This ensures that users have the flexibility to choose a tensor program from the top-k list according to their preferences and the defined metric. 8. Finally, the proposed tensor programs are deployed on the target hardware, and their performance is rigorously evaluated. This end-to-end workflow represents a systematic and efficient approach to enabling the seamless transfer of computational models across different hardware environments. The framework's ability to adapt and fine-tune for specific hardware configurations offers enhanced flexibility and performance optimization. In our study, we utilized the TenSet dataset [11] as the foundation for our baseline measurements. According to the assertions made by the dataset's authors, it is positioned as  
Figure 1: Comprehensive Insight into the Proposed Framework. a resource suitable for generating tensor programs via the application of transfer learning. The dataset's strength lies in its diversity, fostering generalization and encompassing performance data points across multiple platforms, including CPUs from Intel and ARM, as well as NVIDIA GPUs.