Matrix Mapping on Crossbar Memory Arrays with Resistive Interconnects and Its Use in In-Memory Compression of Biosignals
Department of Materials Science and Engineering at Seoul National University, Seoul 08826, Korea
Author to whom correspondence should be addressed.
Micromachines 2019, 10(5), 306; https://doi.org/10.3390/mi10050306
Received: 4 April 2019 / Revised: 29 April 2019 / Accepted: 5 May 2019 / Published: 7 May 2019
(This article belongs to the Special Issue Emerging Memory and Computing Devices in the Era of Intelligent Machines)
Recent advances in nanoscale resistive memory devices offer promising opportunities for in-memory computing with their capability of simultaneous information storage and processing. The relationship between current and memory conductance can be utilized to perform matrix-vector multiplication for data-intensive tasks, such as training and inference in machine learning and analysis of continuous data stream. This work implements a mapping algorithm of memory conductance for matrix-vector multiplication using a realistic crossbar model with finite cell-to-cell resistance. An iterative simulation calculates the matrix-specific local junction voltages at each crosspoint, and systematically compensates the voltage drop by multiplying the memory conductance with the ratio between the applied and real junction potential. The calibration factors depend both on the location of the crosspoints and the matrix structure. This modification enabled the compression of Electrocardiographic signals, which was not possible with uncalibrated conductance. The results suggest potential utilities of the calibration scheme in the processing of data generated from mobile sensing or communication devices that requires energy/areal efficiencies.