scholarly journals Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing

2021 ◽  
Vol 39 (3) ◽  
pp. 510-520
Author(s):  
Shiyu Wang ◽  
Shengbing Zhang ◽  
Xiaoping Huang ◽  
Libo Chang
Keyword(s):  
Remote Sensing ◽  
Power Consumption ◽  
Real Time ◽  
Processing System ◽  
Data Organization ◽  
Single Chip ◽  
Processing Scheme ◽  
Intelligent Processing ◽  
High Efficient ◽  
Processing Architecture

The satellite-borne SAR image intelligent processing system needs to process on-orbit real-time imaging and various tasks of applications, for which reason designing a dedicated high-efficient single-chip multi-processor is of prioritized necessity that can simultaneously satisfy requirements of real-time and low power consumption. Aiming at on-chip data organization and memory access structure, two typical models of SAR(synthetic aperture radar) imaging CSA (chirp scaling) and neural network VGG-11 are analyzed, and then a collaborative computing model for the intelligent processing on remote sensing is extracted. A strip Tile data processing scheme and a dedicated multi-processing architecture is not only proposed, but a data organization and a caching strategy of Tile space synchronization splicing is also presented. In addition, the designed data caching structure among the processing units greatly reduces off-chip access memory bandwidth while supporting parallel pipeline execution of multi-task model. The chip adopts 28 nm CMOS technology featuring with merely 1.83 W of the overall power consumption, whose throughput and energy efficiency reaches 9.89TOPS and 5.4 TOPS/W, respectively. The present architecture can improve real-time performance of the on-orbit remote sensing intelligent processing platform while reducing the complexity of system designing, which is highly adaptive to differentiated expansions according to different models of algorithm.

SMART SENSOR: AN ON-BOARD IMAGE PROCESSING SYSTEM FOR REAL-TIME REMOTE SENSING

2002 ◽  
Vol 02 (03) ◽  
pp. 481-499
Author(s):  
JANE YOU ◽  
DAVID ZHANG
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Compression ◽  
Real Time ◽  
Processing System ◽  
Smart Sensor ◽  
Pipeline Architecture ◽  
New Approach ◽  
Hardware Processing ◽  
Sensor System Design

This paper presents a new approach to smart sensor system design for real-time remote sensing. A combination of techniques for image analysis and image compression is investigated. The proposed algorithms include: (1) a fractional discrimination function for image analysis, (2) a comparison of effective algorithms for image compression, (3) a pipeline architecture for parallel image classification and compression on-board satellites, and (4) a task control strategy for mapping image computing models to hardware processing elements. The efficiency and accuracy of the proposed techniques are demonstrated throughout system simulation.

scholarly journals ON-BOARD GCPS MATCHING WITH IMPROVED TRIPLET LOSS FUNCTION

2020 ◽  
Vol V-2-2020 ◽  
pp. 105-112
Author(s):  
G. Xie ◽  
Z. Zhang ◽  
Y. Zhu ◽  
S. Xiang ◽  
M. Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Power Consumption ◽  
Loss Function ◽  
Intelligent Processing ◽  
Single Frame ◽  
Triplet Loss ◽  
Consumption Model ◽  
Remote Sensing Satellite ◽  
Extraction Model

Abstract. Intelligent remote sensing satellite system is an important direction to solve the problem of intelligent processing on-board. It can realize the real-time on-board intelligent processing of important targets. The accuracy of geometric positioning information is the basis for subsequent intelligent processing. Therefore, this paper corrects the positioning information by GCPs (Ground Control Points) matching on-board. Considering the limited storage and computing performance of satellites, this paper designs a lightweight GCPs deep feature extraction convolutional neural network based on MobileNetV2 as feature extraction model, and trains this network with an improved triplet loss function. The Songshan calibration field images constructed by Wuhan University was used as the GCPs image, and 30,399 image patches were extracted and embedded as GCPs feature library. The size of the GCPs library is a size of 15.3M, and size of the lightweight depth feature extraction model is 9.83M, which can be pre-stored on the satellite for positioning with GCPs matching on-board. In addition, this paper tested feature extraction performance on an embedded device Nvidia Jeston Xavier which simulates the performance of the device on the satellite. At Xavier 30W max power consumption model, a single frame takes 0.005 seconds, and under Xavier 15W power consumption model, a single frame takes 0.009 seconds. At 10W power consumption model, a single frame takes 0.018 seconds, which can meet the performance requirements on the satellite. In addition, the experiments in this paper show that the positioning accuracy is within 30 meters. The work done in this paper will be experimented on the Luojia-3-01 intelligent remote sensing satellite.

scholarly journals On-chip data organization and access strategy for spaceborne SAR real-time imaging processor

2021 ◽  
Vol 39 (1) ◽  
pp. 126-134
Author(s):  
Shiyu Wang ◽  
Shengbing Zhang ◽  
Xiaoping Huang ◽  
Hao Lyu
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Imaging System ◽  
Data Organization ◽  
Real Time Processing ◽  
Time Processing ◽  
Chip Data ◽  
Application Systems ◽  
Sar Imaging ◽  
On Chip

Spaceborne SAR(synthetic aperture radar) imaging requires real-time processing of enormous amount of input data with limited power consumption. Designing advanced heterogeneous array processors is an effective way to meet the requirements of power constraints and real-time processing of application systems. To design an efficient SAR imaging processor, the on-chip data organization structure and access strategy are of critical importance. Taking the typical SAR imaging algorithm-chirp scaling algorithm-as the targeted algorithm, this paper analyzes the characteristics of each calculation stage engaged in the SAR imaging process, and extracts the data flow model of SAR imaging, and proposes a storage strategy of cross-region cross-placement and data sorting synchronization execution to ensure FFT/IFFT calculation pipelining parallel operation. The memory wall problem can be alleviated through on-chip multi-level data buffer structure, ensuring the sufficient data providing of the imaging calculation pipeline. Based on this memory organization and access strategy, the SAR imaging pipeline process that effectively supports FFT/IFFT and phase compensation operations is therefore optimized. The processor based on this storage strategy can realize the throughput of up to 115.2 GOPS, and the energy efficiency of up to 254 GOPS/W can be achieved by implementing 65 nm technology. Compared with conventional CPU+GPU acceleration solutions, the performance to power consumption ratio is increased by 63.4 times. The proposed architecture can not only improve the real-time performance, but also reduces the design complexity of the SAR imaging system, which facilitates excellent performance in tailoring and scalability, satisfying the practical needs of different SAR imaging platforms.

scholarly journals AN INSTANTANEOUS POINT SOURCE DETECTION AND EXTRACTION SYSTEM FOR GEO REMOTE SENSING SATELLITE

2018 ◽  
Vol XLII-3 ◽  
pp. 451-454
Author(s):  
Y. Guo ◽  
Q. Li ◽  
W. Wu
Keyword(s):  
Remote Sensing ◽  
Point Source ◽  
Real Time ◽  
Processing System ◽  
Image Data ◽  
Source Detection ◽  
Source Information ◽  
Real Time Processing ◽  
Time Processing ◽  
Instantaneous Point

To accomplish the task of detecting the instantaneous point source, an on-board information real-time processing system is designed which can process the point-source detection with reconfigurable function. The system has the algorithm reconfigurable function, which can detect and extract the instantaneous point source from the remote sensing image. By using FPGA programming, the satellite target detection and processing algorithm can be update easily. At the same time, the software can be reconfigured to improve the system's information processing capabilities. The system has been verified by simulating real instantaneous source point target image data to meet the real-time processing requirements of instantaneous point source information detection.

FPGA Implement of Multi-Channel Real-Time Correlation Processing System

2013 ◽  
Vol 303-306 ◽  
pp. 1925-1929
Author(s):  
Xin Liu ◽  
Da Jun Sun ◽  
Ting Ting Teng ◽  
Yuan Tian
Keyword(s):  
Real Time ◽  
Processing System ◽  
Large Data ◽  
Time Correlation ◽  
Correlation Processing ◽  
Parallel Mode ◽  
Data Bus ◽  
Low Efficiency ◽  
System Controller ◽  
Processing Architecture

The traditional real-time correlation processing system in FPGA is implemented in parallel mode. It has disadvantages such as high FPGA resource-consuming, low efficiency and poor flexibility. A time-multiplexed processing architecture takes NIOS processor as system controller, connected with preprocessing module, sliding-correlation processor and memories by Avalon data bus. The transmission of large data block out of sliding-correlation processor employs DMA method for its controlling flexibility, the data transmission between computing units and memory units within the processor employs directly memory access to minimum time delay.

scholarly journals Toward the Implementation of an ASIC-Like System on FPGA for Real-Time Video Processing with Power Reduction

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lilia Kechiche ◽  
Lamjed Touil ◽  
Bouraoui Ouni
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Video Processing ◽  
Integrated Design ◽  
Processing System ◽  
Design Flow ◽  
System Level ◽  
Design Environment ◽  
Chip Design ◽  
On Chip

Driven by the importance of energy consumption in system-on-chip design as an evaluation factor, this paper presents a design methodology at the system level to optimize power consumption on ARM-based architecture for real-time video processing. The proposed design flow is based on the interaction between the tool and user optimizations. The tool optimizations are the options and best practices available on the integrated design environment for the Xilinx technology and the target Zynq-7000 architecture. The user methods present methods proposed by the user to optimize power consumption. We used the principles of voltage scaling and frequency scaling techniques for user methods. These two techniques allow energy to be consumed in the proportion of work to be done. The suggested flow is applied on real-time video processing system. The results show power savings for up to 60% with respect to performance and real-time constraints.

Intelligent Processing of Materials

MRS Bulletin ◽  
1988 ◽  
Vol 13 (4) ◽  
pp. 17-20
Author(s):  
H. Thomas Yolken
Keyword(s):  
Real Time ◽  
Processing System ◽  
Value Added ◽  
Process Variable ◽  
Processing Parameters ◽  
Advanced Materials ◽  
Time Data ◽  
Intelligent Processing ◽  
Quality Control Laboratory ◽  
And Performance

Advanced materials can provide specialized properties, or combinations of properties, that cannot be obtained in conventional materials. However, advanced materials generally require unusual processing operations in order to achieve their unique microstructures and the resulting greatly improved properties. These materials also tend to be expensive because of the high value added by unusual processing operations that may be labor intensive. Because the relationships among the processing parameters, microstructure, and resulting material properties and performance are not fully understood, and because the microstructure is difficult to control, reproducibility in these materials is often unsatisfactory. A very promising direction toward overcoming these difficulties involves intelligent processing of materials, a computer-based approach to automatically controlling the evolution of microstructure during processing.In a conventional automated materials processing system, automation involves utilizing sensors to monitor process variables such as temperature and pressure. Data from these sensors are compared with preset values automatically in order to maintain these values with control devices. Nevertheless, the microstructure and properties often experience significant variations. The variations are detected after the fact either by destructive analysis in a quality control laboratory or by nondestructive evaluation (NDE) at the end of the manufacturing process.In contrast, an intelligent processing system utilizes a new class of NDE sensors to characterize the microstructure of the material in real time. Moreover, the real-time data plus data from conventional process variable sensors are transmitted to a computerized decision maker.

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