  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.
