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Title:Neural network approach to automatic target recognition (ATR) on deep in-memory architecture (DIMA)
Author(s):Geng, Hanfei
Contributor(s):Shanbhag, Naresh R.
Subject(s):Automatic target recognition
Synthetic aperture radar
Convolutional neural network
Deep in-memory architecture
Abstract:Automatic target recognition (ATR) has a long history and a wide range of applications. It refers to the ability to recognize targets of interest based on readings collected from sensors. The entire process consists of three stages: target detection, target segmentation, and target recognition. This research project investigates the recognition stage of a particular ATR that takes synthetic aperture radar (SAR) images taken on airborne platforms as input. Traditionally this stage has always taken a statistical approach. Recently a neural-network-based approach has been proposed as well. Given its innate complexity and high energy demand due to data movement, it is essential to adopt this approach in an energy-efficient manner. DIMA is a new computation scheme dedicated to this effort. In contrast to traditional von Neumann architecture, it reduces the high energy costs of data movement between processor and memory through analog computations deeply embedded into the periphery of the bit-cell array (BCA) in the memory. The goal of this project is to demonstrate the benefits of DIMA in the context of neural-network-based ATR. Several floating-point and fixed-point convolutional neural network (CNN) models of SAR-based ATR have been developed. With the noise model of DIMA, Monte Carlo simulation was performed on a popular ATR dataset called Moving and Stationary Target Acquisition and Recognition (MSTAR) to study the effect on CNN model’s accuracy.
Issue Date:2019-05
Date Available in IDEALS:2019-06-19

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