Analog Neural Computing with Super-resolution Memristor Crossbars

TL;DR

This paper introduces a super-resolution technique for memristor crossbars, enhancing their conductance resolution by combining multiple memristors per node, thereby improving analog neural network performance.

Contribution

The paper proposes a novel super-resolution method that increases memristor crossbar resolution using multiple memristors per node, enabling more precise analog neural network implementations.

Findings

Enhanced conductance resolution with multiple memristors per node

Improved analog neural network layer performance

Potential for more accurate neuromorphic computing

Abstract

Memristor crossbar arrays are used in a wide range of in-memory and neuromorphic computing applications. However, memristor devices suffer from non-idealities that result in the variability of conductive states, making programming them to a desired analog conductance value extremely difficult as the device ages. In theory, memristors can be a nonlinear programmable analog resistor with memory properties that can take infinite resistive states. In practice, such memristors are hard to make, and in a crossbar, it is confined to a limited set of stable conductance values. The number of conductance levels available for a node in the crossbar is defined as the crossbar's resolution. This paper presents a technique to improve the resolution by building a super-resolution memristor crossbar with nodes having multiple memristors to generate r-simplicial sequence of unique conductance values. The wider the range and number of conductance values, the higher the crossbar's resolution. This is particularly useful in building analog neural network (ANN) layers, which are proven to be one of the go-to approaches for forming a neural network layer in implementing neuromorphic computations.