标题： 类比：利用迁移学习加速新兴硬件上的稀疏张量程序 显示英文标题

标题： COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning

摘要： 稀疏张量程序在深度学习和图分析中至关重要，推动了对优化处理的需求。为了满足这一需求，正在开发专门的硬件加速器。然而，优化这些程序以适应加速器面临两个挑战：程序性能对稀疏输入的变化非常敏感，并且早期阶段的加速器依赖昂贵的模拟器。因此，用于在通用硬件上优化此类程序的基于机器学习的成本模型往往对早期阶段的加速器无效，因为它们需要大量数据集进行适当的训练。为此，我们引入了COGNATE，这是一种新颖的框架，它利用来自通用硬件（例如CPU）的廉价数据样本来训练成本模型，然后在新兴硬件上进行少量微调。COGNATE利用了跨硬件平台的输入特征的同质性，同时有效缓解异质性，使得仅需加速器特定模型所需数据样本的5%，即可实现相当的性能。我们进行了广泛的实验，证明COGNATE优于现有技术，在SpMM上的平均加速比为1.47倍（最高达5.46倍），在SDDMM上的平均加速比为1.39倍（最高达4.22倍）。 显示英文摘要

摘要： Sparse tensor programs are essential in deep learning and graph analytics, driving the need for optimized processing. To meet this demand, specialized hardware accelerators are being developed. Optimizing these programs for accelerators is challenging for two reasons: program performance is highly sensitive to variations in sparse inputs, and early-stage accelerators rely on expensive simulators. Therefore, ML-based cost models used for optimizing such programs on general-purpose hardware are often ineffective for early-stage accelerators, as they require large datasets for proper training. To this end, we introduce COGNATE, a novel framework that leverages inexpensive data samples from general-purpose hardware (e.g., CPUs) to train cost models, followed by few-shot fine-tuning on emerging hardware. COGNATE exploits the homogeneity of input features across hardware platforms while effectively mitigating heterogeneity, enabling cost model training with just 5% of the data samples needed by accelerator-specific models to achieve comparable performance. We conduct extensive experiments to demonstrate that COGNATE outperforms existing techniques, achieving average speedups of 1.47x (up to 5.46x) for SpMM and 1.39x (up to 4.22x) for SDDMM.