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A concept of current-mode long-term analog memory for neural-network learning on silicon

机译:在硅上进行神经网络学习的电流模式长期模拟存储器的概念

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A new concept and CMOS implementation of an analog current-mode memory with increased retention time is presented. Because the memory is of a capacitive type, there are difficulties with long-term storing the written information, when its basic form is used [3]–[4]. To overcome this problem, we propose applying a positive feedback which ensures obtaining the same base potential of the memory sample & hold switch as the potential across the holding capacitor. As a result, holding time of the memory has been increased by several orders of magnitude compared to that of the basic memory with no enlargement in the memory writing time. Simulation results are shown to be in a good agreement with theoretic considerations. The proposed memory cell can operate with a low power losses when properly designed. An influence of transistor size mismatching, characteristic for analog circuits, on the memory properties is also discussed and, although visible, appears not to be critical.
机译:提出了具有更长保留时间的模拟电流模式存储器的新概念和CMOS实现。因为存储器是电容型的,所以当使用书面形式的基本形式时,很难长期存储书面信息[3] – [4]。为了克服这个问题,我们建议采用正反馈,以确保获得与存储电容器上的电势相同的存储器采样和保持开关的基本电势。结果,与基本存储器相比,存储器的保持时间已经增加了几个数量级,而没有增加存储器写入时间。仿真结果表明与理论上的考虑非常吻合。如果设计合理,建议的存储单元可以在低功耗下运行。还讨论了晶体管尺寸失配(模拟电路的特性)对存储器特性的影响,尽管可见,但似乎并不重要。

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