Morro Antoni, Canals Vincent, Oliver Antoni, Alomar Miquel L, Rossello Josep L
Electronic Engineering Group, Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Balears, Spain.
PLoS One. 2015 May 8;10(5):e0124176. doi: 10.1371/journal.pone.0124176. eCollection 2015.
Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories. We design pulse-based stochastic-logic blocks to obtain an efficient pattern recognition system. The proposed architecture speeds up the screening process of huge databases by a factor of 7 when compared to a conventional digital implementation using the same hardware area.
在我们这个信息驱动的社会中，非常需要能够在合理时间内处理大量数据的最小硬件实现。在这项工作中，我们回顾了随机计算在基于概率的大型数据库集模式识别分析中的应用。所提出的技术在于并行架构的硬件实现，该架构对数据相对于不同预存储类别进行相似性搜索。我们设计基于脉冲的随机逻辑块以获得高效的模式识别系统。与使用相同硬件面积的传统数字实现相比，所提出的架构将大型数据库的筛选过程加速了7倍。
