AUTHOR=Agarwal Sapan , Quach Tu-Thach , Parekh Ojas , Hsia Alexander H. , DeBenedictis Erik P. , James Conrad D. , Marinella Matthew J. , Aimone James B. 

TITLE=Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding

JOURNAL=Frontiers in Neuroscience

VOLUME=9

YEAR=2016

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2015.00484

DOI=10.3389/fnins.2015.00484

ISSN=1662-453X

ABSTRACT=<p>The exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an <italic>N</italic> × <italic>N</italic> crossbar, these two kernels can be <italic>O</italic>(<italic>N</italic>) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (<italic>O</italic>(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an <italic>O</italic>(<italic>N</italic>) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.</p>