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Analog hardware implementation of spike-based delayed feedback reservoir computing system

机译：基于峰值的延迟反馈储层计算系统的模拟硬件实现

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The rate of enhancement is starting to saturate and slow down which indicates the end of Moore's prediction due to the fundamental performance limits of the chips. The need of breaking through the barrier has directed researchers into several directions, for instance, novel computing architecture. Reservoir computing, a novel concept in the field of machine learning, has emerged over the past few years. Combined the memory and spatio-temporal processing of recurrent neural networks, reservoir computing possesses the capability of processing temporal information. In this paper, we present an analog hardware implementation of delayed feedback reservoir computing system. We build a new class of computationally efficient spike timing-dependent encoders and delay-based reservoirs within reservoir networks. This approach allows us to avoid using power-consuming analog-to-digital converters (ADCs) and operational amplifiers (Op-AMPs), resulting in significant savings in power requirements and design area.
机译：增强速度开始达到饱和并放慢速度，这表明由于芯片的基本性能限制，Moore的预测已结束。突破障碍的需求已将研究人员引导到了多个方向，例如，新颖的计算体系结构。在过去的几年中，已经出现了存储计算，这是机器学习领域中的一个新颖概念。结合递归神经网络的存储和时空处理，储层计算具有处理时空信息的能力。在本文中，我们提出了延迟反馈储层计算系统的模拟硬件实现。我们在油藏网络内建立了一类新的计算有效的与峰值定时相关的编码器和基于延迟的油藏。这种方法使我们避免使用耗电的模数转换器（ADC）和运算放大器（Op-AMP），从而大大节省了功率要求和设计面积。

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