全平行模式匹配引擎与动态适应汉明或曼哈顿距离

H. Mattausch, N. Omori, S. Fukae, T. Koide, T. Gyoten
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引用次数: 22

摘要

本文提出的模式匹配引擎通过模式编码实现距离度量的适应性,因此可以覆盖广泛的高性能实时应用。缩短最接近匹配时间的关键是紧凑的全并行联想记忆核心。在0.6 /spl mu/m CMOS技术下,9.75 mm/sup /测试电路的性能大约相当于一台32位ITOPS计算机。测试电路建议可能的模式长度/spl /768等效位,>10/sup /7 /模式/秒吞吐量,<1.13%的赢家输入距离误差和<1.35 mW的每个参考模式的功耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully-parallel pattern-matching engine with dynamic adaptability to Hamming or Manhattan distance
The proposed pattern-matching engine achieves distance-measure adaptability through pattern encoding and can therefore cover a wide range of high-performance real-time applications. Key to short nearest-match times is a compact fully-parallel associative-memory core. The performance of a 9.75 mm/sup 2/ test-circuit in 0.6 /spl mu/m CMOS technology is about equivalent to a 32 bit computer with ITOPS. The test-circuit suggests possible pattern length /spl ges/768 equivalent bit, >10/sup 7/ pattern/sec throughput, <1.13% winner-input-distance error and <1.35 mW power dissipation per reference pattern.
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