Matrix Mapping on Crossbar Memory Arrays with Resistive Interconnects and Its Use in In-Memory Compression of Biosignals

Yoon Kyeung Lee,Woohyun Kim,Eui-Sang Park,Jeong Woo Jeon,Chanyoung Yoo,Cheol Seong Hwang,Manick Ha

doi:10.3390/mi10050306

Abstract

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.

Highlights

Emerging classes of mobile electronic devices offer attractive capabilities for real-time analytics of the physical world through the connection to central computing systems
A subset of the processing operations can be categorized as a linear transformation which can be expressed as a matrix-vector multiplication (MVM)
The MVM can be performed in an analogue domain using a resistive memory crossbar array by storing the matrix values as the conductance of the memory cell

Summary

Introduction

Emerging classes of mobile electronic devices offer attractive capabilities for real-time analytics of the physical world through the connection to central computing systems. One of the critical challenges in this emerging Internet of Things (IoT) is the instantaneous extraction of relevant information from the abundant data with the limited power and communication bandwidth for data transmission This challenge demands smart components on the edge of the mobile devices that can filter, compress, or classify the data outputs onsite [1,2,3,4]. Hu et al first introduced a comprehensive crossbar array model for MVM, and applied it to the training of neural network for pattern recognition [9] This simulation-based optimization of the conductance minimizes the time and power consumption to post-process the outputs and provides explanation for the computational outputs with given circuits. The findings suggest a possible route to overcome difficulties in analogue computing in realizing diverse edge computing devices for onsite data processing

Calibration Factor for Matrix Mapping on Proposed Crossbar Model

Results and Discussion

Conclusions