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Integrated Circuit Designs for Reservoir Computing and Machine Learning

机译:用于储层计算和机器学习的集成电路设计

摘要

An integrated circuit device for reservoir computing can include a weighted input layer, an unweighted, asynchronous, internal recurrent neural network made up of nodes having binary weighting, and a weighted output layer. Weighting of output signals can be performed using predetermined weighted sums stored in memory. Application specific integrated circuit (ASIC) embodiments may include programmable nodes. Characteristics of the reservoir of the device can be tunable to perform rapid processing and pattern recognition of signals at relatively large rates.
机译:用于储存器计算的集成电路装置可以包括加权输入层,由具有二进制加权的节点和加权输出层构成的未加权的异步的内部复制神经网络。 可以使用存储在存储器中的预定加权之和来执行输出信号的加权。 应用特定集成电路(ASIC)实施例可以包括可编程节点。 可以调谐装置的储存器的特性,以执行相对大的速率的快速处理和模式识别。

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