CrossSim

CrossSim is a GPU-accelerated, Python-based crossbar simulator designed to model analog in-memory computing for any application that relies on matrix operations. This includes neural networks, signal processing, solving linear systems, and many more. It is an accuracy simulator and co-design tool that was developed to address how analog hardware effects in resistive crossbars impact the quality of the algorithm solution.

CrossSim has a Numpy-like API that allows different algorithms to be built on resistive memory array building blocks. CrossSim cores can be used as drop-in replacements for Numpy in application code to emulate deployment on analog hardware. CrossSim also provides both Torch and Keras compatible implementations of many analog-compatible neural network layers including linear and convolutional layers.

CrossSim can model device and circuit non-idealities such as arbitrary programming errors, conductance drift, cycle-to-cycle read noise, and precision loss in analog-to-digital conversion (ADC). It also uses a fast, internal circuit simulator to model the effect of parasitic metal resistances on accuracy.

CrossSim also supports simulating systems with a wide range of data representation strategies including using multiple devices per value (i.e., weight bit slicing) and multiple approaches to representing negative numbers. Each of these components provides a modular interface to allow users to quickly implement models of new research ideas and explore the effects within the context of a larger applications.