Deep neural network inference accelerators are rapidly growing in importance as we turn to massively parallelized processing beyond GPUs and ASICs. The dominant operation in feedforward inference is the multiply-and-accumlate process, where each column in a crossbar generates the current response of a single neuron. As a result, memristor crossbar arrays parallelize inference and image processing tasks very efficiently. In this brief, we present a 3-D active memristor crossbar array `CrossStack', which adopts stacked pairs of Al/TiO2/TiO2-x/Al devices with common middle electrodes. By designing CMOS-memristor hybrid cells used in the layout of the array, CrossStack can operate in one of two user-configurable modes as a reconfigurable inference engine: 1) expansion mode and 2) deep-net mode. In expansion mode, the resolution of the network is doubled by increasing the number of inputs for a given chip area, reducing IR drop by 22%. In deep-net mode, inference speed per-10-bit convolution is improved by 29\% by simultaneously using one TiO2/TiO2-x layer for read processes, and the other for write processes. We experimentally verify both modes on our $10\times10\times2$ array.

中文翻译：

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CrossStack：3-D可重配置RRAM Crossbar推理引擎

随着我们转向GPU和ASIC以外的大规模并行处理，深度神经网络推理加速器的重要性正在迅速增长。前馈推断的主要操作是乘法和累加过程，其中交叉开关中的每一列都会生成单个神经元的当前响应。结果，忆阻器交叉开关阵列非常有效地并行化了推理和图像处理任务。在本简介中，我们介绍了一种3-D有源忆阻器交叉开关阵列“ CrossStack”，该阵列采用成对的Al / TiO2 / TiO2-x / Al器件与公共中间电极堆叠在一起。通过设计阵列布局中使用的CMOS忆阻混合单元，CrossStack可以作为可重新配置的推理引擎以两种用户可配置模式之一运行：1）扩展模式和2）深网模式。在扩展模式下，通过增加给定芯片面积的输入数量，使网络分辨率提高一倍，从而使IR下降降低22％。在深网模式下，通过同时将一个TiO2 / TiO2-x层用于读取过程，将另一个TiO2 / TiO2-x层用于写入过程，将每10位卷积的推理速度提高了29％。我们在$ 10 \ times10 \ times2 $数组中实验性地验证了这两种模式。