Energy Scaling Advantages of Resistive Memory Crossbar Based Computation and Its Application to Sparse Coding - Agarwal, Sapan; Quach, Tu-Thach; Parekh, Ojas; Hsia, Alexander H.; DeBenedictis, Erik P.; James, Conrad D.; Marinella, Matthew J.; Aimone, James B. (Frontiers in Neuroscience, 2016)
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 advantages 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 NxN crossbar, these two kernels are at a minimum O(N) more energy efficient than a digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(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 O(N) reduction in energy for the entire algorithm. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.