  Resistive crossbars designed with non-volatile memory devices have emerged as
promising building blocks for Deep Neural Network (DNN) hardware, due to their
ability to compactly and efficiently realize vector-matrix multiplication
(VMM), the dominant computational kernel in DNNs. However, a key challenge with
resistive crossbars is that they suffer from a range of device and circuit
level non-idealities such as interconnect parasitics, peripheral circuits,
sneak paths, and process variations. These non-idealities can lead to errors in
VMMs, eventually degrading the DNN's accuracy. It is therefore critical to
study the impact of crossbar non-idealities on the accuracy of large-scale
DNNs. However, this is challenging because existing device and circuit models
are too slow to use in application-level evaluations.
  We present RxNN, a fast and accurate simulation framework to evaluate
large-scale DNNs on resistive crossbar systems. RxNN splits and maps the
computations involved in each DNN layer into crossbar operations, and evaluates
them using a Fast Crossbar Model (FCM) that accurately captures the errors
arising due to crossbar non-idealities while being four-to-five orders of
magnitude faster than circuit simulation. FCM models a crossbar-based VMM
operation using three stages - non-linear models for the input and output
peripheral circuits (DACs and ADCs), and an equivalent non-ideal conductance
matrix for the core crossbar array. We implement RxNN by extending the Caffe
machine learning framework and use it to evaluate a suite of six large-scale
DNNs developed for the ImageNet Challenge. Our experiments reveal that
resistive crossbar non-idealities can lead to significant accuracy degradations
(9.6%-32%) for these large-scale DNNs. To the best of our knowledge, this work
is the first quantitative evaluation of the accuracy of large-scale DNNs on
resistive crossbar based hardware.
