RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars

Understand

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.