一句话：提出 FlexAmata，使字母表规模各异的自动机应用能通用高效地映射到空间自动机加速器。
模式匹配是许多大数据应用的重要任务，通常可映射为有限自动机。近期研究利用空间架构（如 FPGA 和存内计算）加速自动机处理，将模式直接映射到大规模并行硬件元素。然而，现有自动机加速架构均基于固定的 8 位符号处理，这一设计源自 ASCII 编码假设。实际模式匹配应用的字母表规模差异巨大，从仅几个唯一符号到数十亿个唯一符号不等，因此固定符号宽度很难为广泛的应用提供通用且高效的映射。FlexAmata 针对该问题，提出一种通用且高效的适配方案，使不同字母表规模的自动机应用能够映射到空间自动机处理加速器。摘要未报告具体实验评估。
Pattern matching, especially for complex patterns with many variations, is an important task in many big-data applications and maps well to finite automata. Recently, a variety of research has focused on hardware acceleration of automata processing, especially via spatial architectures that directly map the patterns to massively parallel hardware elements, such as in FPGAs and in-memory solutions. We observed that all existing automata-acceleration architectures are designed based on fixed, 8-bit symbol processing, derived from ASCII processing. However, the alphabet size in pattern-matching applications varies from just a few up to billions of unique symbols. This makes it difficult to provide a universal and efficient mapping of this wide variety of automata applications to existing automata accelerators.
DOI 原文 ·
@inproceedings{paperbot5422,
title = {FlexAmata: A Universal and Efficient Adaption of Applications to Spatial Automata Processing Accelerators},
author = {Elaheh Sadredini and Reza Rahimi and Marzieh Lenjani and Mircea Stan and Kevin Skadron},
booktitle = {ASPLOS},
year = {2020},
doi = {10.1145/3373376.3378459}
}