一句话：提出软硬件协同的内存位向量自动机处理器 BVAP，高效支持正则表达式有界重复。
正则模式匹配广泛用于文本处理、恶意软件检测、网络安全和生物信息学。现有内存自动机处理器在能效和内存效率上优于通用平台，但普遍缺乏对正则表达式中广泛使用的有界重复结构的有效支持。本文提出 BVAP，一种软硬件协同设计的内存位向量自动机处理器。其核心是新的理论模型——动作同质非确定位向量自动机（AH-NBVA），并配套高效硬件实现与将正则表达式转换为硬件配置的编译器。作者用周期精确模拟器在 28nm CMOS 工艺下评估，在多个真实基准上相比 CA、eAP 和 CAMA，能效提高 67-95%，面积减少 42-68%。
Regular pattern matching is pervasive in applications such as text processing, malware detection, network security, and bioinformatics. Recent studies have demonstrated specialized in-memory automata processors with superior energy and memory efficiencies than existing computing platforms. Yet, they lack efficient support for the construct of bounded repetition that is widely used in regular expressions (regexes). This paper presents BVAP, a software-hardware co-designed in-memory Bit Vector Automata Processor. It is enabled by a novel theoretical model called Action-Homogeneous Non-deterministic Bit Vector Automata (AH-NBVA), its efficient hardware implementation, and a compiler that translates regexes into hardware configurations. BVAP is evaluated with a cycle-accurate simulator in a 28nm CMOS process, achieving 67-95% higher energy efficiency and 42-68% lower area, compared to state-of-the-art automata processors (CA, eAP, and CAMA), across a set of real-world benchmarks.
@inproceedings{WenKGM024,
title = {BVAP: Energy and Memory Efficient Automata Processing for Regular Expressions with Bounded Repetitions},
author = {Ziyuan Wen and Lingkun Kong and Alexis Le Glaunec and Konstantinos Mamouras and Kaiyuan Yang},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640412}
}