Graph-Theoretically Optimal Memory Banking for Stencil-Based Computing Kernels

Juan Escobedo, Mingjie Lin
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引用次数: 15

Abstract

High-Level Synthesis (HLS) has advanced significantly in compiling high-level "soft»» programs into efficient register-transfer level (RTL) "hard»» specifications. However, manually rewriting C-like code is still often required in order to effectively optimize the access performance of synthesized memory subsystems. As such, extensive research has been performed on developing and implementing automated memory optimization techniques, among which memory banking has been a key technique for access performance improvement. However, several key questions remain to be answered: given a stencil-based computing kernel, what constitutes an optimal memory banking scheme that minimizes the number of memory banks required for conflict-free accesses? Furthermore, if such an optimal memory banking scheme exists, how can an FPGA designer automatically determine it? Finally, does any stencil-based kernel have the optimal banking scheme? In this paper we attempt to optimally solve memory banking problem for synthesizing stencil-based computing kernels with well-known theorems in graph theory. Our graph-based methodology not only computes the minimum memory partition factor for any given stencil, but also exploits the repeatability of coloring entire memory access conflict graph, which significantly improves hardware efficiency.
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基于模板的计算核的图理论最优内存存储
高级合成(High-Level Synthesis, HLS)在将高级“软”程序编译成高效的寄存器-传输级别(register-transfer level, RTL)方面取得了重大进展。“硬»»规范。然而,为了有效地优化合成内存子系统的访问性能,仍然经常需要手动重写类c代码。因此,在开发和实现自动内存优化技术方面进行了广泛的研究,其中内存银行已成为提高访问性能的关键技术。然而,仍然有几个关键问题需要回答:给定一个基于模板的计算内核,什么构成了一个最优的内存银行方案,使无冲突访问所需的内存银行数量最小化?此外,如果存在这样的最佳内存银行方案,FPGA设计者如何自动确定它?最后,是否有基于模板的内核具有最优的银行方案?在本文中,我们尝试用图论中众所周知的定理来最优地解决基于模板的计算核合成中的内存存储问题。我们的基于图的方法不仅计算任何给定模板的最小内存分区因子,而且利用了整个内存访问冲突图着色的可重复性,从而显着提高了硬件效率。
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Architecture and Circuit Design of an All-Spintronic FPGA Session details: Session 6: High Level Synthesis 2 A FPGA Friendly Approximate Computing Framework with Hybrid Neural Networks: (Abstract Only) Software/Hardware Co-design for Multichannel Scheduling in IEEE 802.11p MLME: (Abstract Only) Session details: Special Session: Deep Learning
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