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Oscillator Array Models for Associative Memory and Pattern Recognition

机译:用于关联记忆和模式识别的振荡器阵列模型

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摘要

Brain-inspired arrays of parallel processing oscillators represent an intriguing alternative to traditional computational methods for data analysis and recognition. This alternative is now becoming more concrete thanks to the advent of emerging oscillators fabrication technologies providing high density packaging and low power consumption. One challenging issue related to oscillator arrays is the large number of system parameters and the lack of efficient computational techniques for array simulation and performance verification. This paper provides a realistic phase-domain modeling and simulation methodology of oscillator arrays which is able to account for the relevant device nonidealities. The model is employed to investigate the associative memory performance of arrays composed of resonant LC oscillators.
机译:灵感来自大脑的并行处理振荡器阵列代表了用于数据分析和识别的传统计算方法的有趣替代品。由于新兴的振荡器制造技术的出现,这种选择现在变得更加具体,振荡器制造技术提供了高密度封装和低功耗。与振荡器阵列有关的一个具有挑战性的问题是大量的系统参数以及缺乏用于阵列仿真和性能验证的高效计算技术。本文提供了一种可行的振荡器阵列相域建模和仿真方法,该方法能够解决相关的器件非理想性问题。该模型用于研究由谐振LC振荡器组成的阵列的关联存储性能。

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