发表机构
University of Delaware; Pacific Northwest National Lab; UNC Charlotte（特拉华大学; 太平洋西北国家实验室; 北卡罗来纳大学夏洛特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对硬件-软件划分问题，提出基于有向路径宽度的精确固定参数可处理算法，涵盖多种现有形式，并在实际任务图上实现高达200倍加速。
AI 中文摘要
在为特定计算密集型任务（如评估或训练神经网络）优化计算架构时，识别哪些计算子任务将带来最大的成本（如墙钟时间或能耗）降低至关重要。这一问题被称为硬件-软件（HS）划分，它有多种形式，其中许多是NP难问题。在本文中，我们将定义一族HS划分形式，这些形式允许基于给定任务图的有向路径宽度（directed pathwidth）的精确固定参数可处理算法，并证明这一族问题包含多种现有形式，如完工时间最小化。最后，我们研究了实际应用中出现的具有小有向路径宽度的任务图，并表明我们的算法相较于使用Gurobi ILP库的类似线性规划方法，可以提供高达200倍的加速。
英文摘要
When optimizing computing architecture for specific computationally intensive tasks, such as evaluating or training a neural network, it is important to identify what computational subtasks will offer the greatest decrease in cost (e.g., wall clock time or energy usage). This problem is known as Hardware-Software (HS) Partitioning, and it has a variety of formulations, many of which are NP-Hard. In this paper, we will define a family of HS Partitioning formulations which admit an exact fixed parameter tractable algorithm based on the directed pathwidth of the given task graph and show that this family of problems contains multiple existing formulations such as makespan minimization. Finally, we examine task graphs with small directed pathwidth that arise in real world applications and show that our algorithm can provide a speed up of up to 200x over a comparable linear programming approach utilizing the Gurobi ILP Library.
Comments6 pages, 4 figures, published HPEC 2026
