Optimized Allocation of FPGA Memory for Image Processing

ABSTRACT Memory is the most restricting component for use in the Field Programmable Gate Array (FPGA) for elevated level picture preparation, which requires total casing (s) to be put away in the general area. Since the FPGA on-chip memory work is restricted, utilizing these assets adequately is essential to meet the exhibition, size, and intensity utilization limitations. This article aims to diminish asset utilization and force utilization, explore the picture preparing significant level prompting energy preservation, and understand the portion of on-chip memory assets included the FPGA altogether. The proposed memory engineering strategy, notwithstanding equipment depiction language, the plan of the significant levels of combination, is generally memory inhabitance, you can lessen the force utilization. On-chip formal force model dependent on the memory design choices will demonstrate whether the dividing calculation is higher than in how the regular procedure. Contrasted with business FPGA blend and elevated level union instruments, our outcomes, the proposed calculation shows that can bring about higher effectiveness, the number and size of edges that can be obliged in the uplink outline increment, about buffer% diminished unique force utilization. In the utilization of optical stream and mean movement following speaking to modern calculations, division calculation, as appeared by our exploratory information, without influencing the exhibition, it can lessen the separate all-out force.

[1]  Thomas Greiner,et al.  A new FPGA based architecture to improve performance of deflectometry image processing algorithm , 2017, 2017 40th International Conference on Telecommunications and Signal Processing (TSP).

[2]  Farida Cheriet,et al.  Memory efficient Multi-Scale Line Detector architecture for retinal blood vessel segmentation , 2016, 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP).

[3]  Guoman Liu,et al.  Implementation of multiport memory access arbitration logic in high speed image system , 2015, 2015 8th International Congress on Image and Signal Processing (CISP).

[4]  Mohd Fadzli Mohd Salleh,et al.  Efficient FDWT/IDWT hardware implementation with line-based and dual-scan image memory accesses , 2017, TENCON 2017 - 2017 IEEE Region 10 Conference.

[5]  Qian Mu,et al.  The Application of Coal Cleaning Detection System Based on Embedded Real-Time Image Processing , 2013, 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation.

[6]  Mohamed Abid,et al.  Hardware resource utilization optimization in FPGA-based Heterogeneous MPSoC architectures , 2015, Microprocess. Microsystems.

[7]  Leilei Wang,et al.  Physical education image analysis based on virtual crowd simulation and FPGA , 2020, Microprocess. Microsystems.

[8]  John McAllister,et al.  Valved dataflow for FPGA memory hierarchy synthesis , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Jose A. Moreno-Cadenas,et al.  Time-multiplexing cellular neural network in FPGA for image processing , 2017, 2017 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[10]  P. Rajesh Kumar,et al.  Implementation of real time image processing system with FPGA and DSP , 2016, 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).

[11]  Zhihong Huang,et al.  Exploring Resource-Efficient Acceleration Algorithm for Transposed Convolution of GANs on FPGA , 2019, 2019 International Conference on Field-Programmable Technology (ICFPT).

[12]  Yiran Chen,et al.  An FPGA Design Framework for CNN Sparsification and Acceleration , 2017, 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).

[13]  Quing Zhu,et al.  FPGA-based reconfigurable processor for ultrafast interlaced ultrasound and photoacoustic imaging , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[14]  Liang Kun,et al.  Development of Image Processing System Based on DSP and FPGA , 2007, 2007 8th International Conference on Electronic Measurement and Instruments.

[15]  K. R. Rekha,et al.  FPGA Implementation of Image Block Generation and Color Space Conversion for the Gaussian Mixture Model , 2017, 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT).

[16]  Mohamed Hedi Bedoui,et al.  FPGA dedicated hardware architecture of 3D image reconstruction: Marching cubes algorithm , 2014, 2014 World Symposium on Computer Applications & Research (WSCAR).

[17]  D. Seidner A low cost FPGA image processor architecture with external line memory , 2012, 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel.

[18]  Anapu Kiran Kumar,et al.  On-chip memory for image processing applications based on FPGA , 2016, 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES).

[19]  Deming Chen,et al.  High-performance video content recognition with long-term recurrent convolutional network for FPGA , 2017, 2017 27th International Conference on Field Programmable Logic and Applications (FPL).