REVIEW 3 major objections 6 minor 60 references
Design and Implementation of Washing Machine HUD Using FPGAs
T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read A Spartan-3E FPGA can run a complete washing-machine simulator and drive its own VGA heads-up display.
desk verdict A candid student project write-up with honest debugging details, but no research contribution and no evidence for the central VGA claim; fine as a lab report, not for peer review. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The argument rides on the top-level Verilog module wm_top.v and its three coordinated subsystems: an FSM that sequences the six wash states and owns the load-dependent timer counters; a rotary_filter.v module that synchronizes and decodes the encoder's quadrature signals into a one-clock direction pulse; and a vga_sync.v module that generates HSYNC, VSYNC, and pixel addressing for the 640x480 display. The VGA domain runs on a divided 25 MHz clock, and synchronizers bridge signals between the 50 MHz FSM domain and the 25 MHz display domain. The HUD maps the current FSM state to color-coded screen regions, which is what makes the machine's behavior visible to the user.
What would settle it
Load the finished bitstream onto a Spartan-3E board and connect it to a VGA monitor known to require the exact 25.175 MHz pixel clock; if the monitor reports 'mode not supported' or never locks to the HUD, the paper's display claim fails.
Extended reading notes
Core claim
The central claim is that a Spartan-3E FPGA, programmed in Verilog, can implement a complete washing-machine controller and its graphical interface in one device. A finite state machine governs the cycle with six states—Fill, Wash, Drain, Rinse, Spin, and Hold—where the timings in each state are scaled by the selected load size, small, medium, or large. A rotary encoder with a quadrature filter selects the load, mechanical buttons provide start, reset, and door commands, and a VGA controller driven at 25 MHz renders a color-coded HUD that reflects the current state. The paper reports that the integrated design used 65% of the Spartan-3E's logic slices and 10% of its block RAM, and that hardware demonstrations confirmed stable VGA output and correct state transitions including the door-open safety pause.
Load-bearing premise
The design assumes that a standard VGA monitor will accept a 25 MHz pixel clock even though the VESA specification for 640x480 at 60 Hz calls for 25.175 MHz.
Editorial extensions
If this is right
- A single FPGA can handle appliance sequencing, input debouncing, quadrature decoding, safety interlocks, and VGA output without a separate microcontroller.
- The same finite state machine can be retimed for other cycle sequences or load profiles by changing counter values rather than hardware.
- The rotary encoder filter and shift-register debouncing are reusable modules for any mechanical input on the Spartan-3E.
- The color-coded HUD approach gives a template for adding graphical status displays to other FPGA-based demonstrations.
- The design fits in about 65% of the Spartan-3E's logic slices, leaving room for added HUD features such as text or progress bars.
Reading between the lines
- Beyond the paper: the HUD's pixel-to-state mapping suggests a general recipe for turning any FSM into a graphical display, so the same top-level split could be reused in student projects on other boards.
- The only uncertain part of the design is monitor tolerance, so a straightforward follow-up would be to test the bitstream across several monitors and, if needed, generate a true 25.175 MHz pixel clock with a phase-locked loop.
- The door-button edge-detect workaround is a practical lesson for FSM design, but it also points to a cleaner general pattern: treat safety inputs as pulses, not levels, unless the hardware enforces latching.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper reports a student FPGA project that implements a washing-machine controller on a Xilinx Spartan-3E board. The system comprises an FSM with Fill/Wash/Drain/Rinse/Spin/Hold states, a rotary encoder for load-size selection, debounced buttons for start/reset and door status, a timing module with load-dependent counters, and a VGA-based HUD that displays the current state. The manuscript gives a modular design narrative, partial Verilog fragments for rotary filtering and door logic, and qualitative claims of successful simulation and hardware validation. It also reports resource utilization of 65% of logic slices and 10% of block RAMs, and contains a large block of self-citations in the Future Work section.
Significance. If the system works as claimed, the paper would be a useful educational case study in FPGA-based embedded control and VGA interfacing. The design is clearly described at the block level, and the authors identify real practical issues such as switch debouncing, quadrature decoding, and UCF constraints. However, the significance is limited by the absence of reproducible evidence: no testbench waveforms, no VGA display captures, no synthesis or timing reports, and no measured verification of the non-standard VGA pixel clock. The central claim that the complete system operates on the Spartan-3E board therefore remains unverified. The paper's contribution is more of a project report than a validated design.
major comments (3)
- [Section 2.2 / Section 4.1] The VGA pixel-clock assumption is load-bearing and unverified. The text states that a 25 MHz clock divider is used because the VESA 640x480@60 spec calls for 25.175 MHz, and asserts that 'the VGA input can down clock and latch onto a slow clock if needed' without any reference or measurement. Section 4.1 then claims adherence to strict horizontal (31.77 us) and vertical (16.68 ms) sync intervals, but with a 25 MHz pixel clock and the usual 800 total pixels per line, the line period is 32 us, not 31.77 us; only 25.175 MHz yields the quoted numbers. If the target monitor rejects the 25 MHz timing, the HUD, a central deliverable, fails. The paper must either provide evidence that the specific monitor locked to the non-standard timing, or use a DCM/PLL to generate 25.175 MHz, and report the resulting measured sync intervals.
- [Sections 1.4, 3.5, 4.2] The claim of 'rigorous evaluation' is not supported by included evidence. The paper states that functional verification used simulation waveforms, state transitions, timer accuracy, and practical hardware demonstrations, but none of these artifacts appear in the manuscript: there are no testbench code listings, no waveform captures, no VGA display photographs or frame captures, no measured timings, and no synthesis or implementation reports. The only Verilog fragments are partial, and the exact counter values for vga_sync.v are not given. Consequently, an independent reader cannot confirm the central claim that the FSM, timer, rotary encoder, and VGA HUD work together on the Spartan-3E board.
- [Section 3.3 / Section 3.4] The synchronization between the 50 MHz FSM domain and the 25 MHz VGA domain is described only qualitatively. The text mentions 'implementing synchronizers to safely bridge signals' but provides no code or analysis for the state signals displayed on the HUD. The door-logic always block in Section 3.4 uses 'posedge clk_25MHz or posedge BTNS', treating a button input as an asynchronous clock event; this is not a standard synchronization structure and its behavior on the Spartan-3E is unclear. Without a concrete synchronizer design or a documented two-flop stage for the FSM state, the HUD could display metastable or stale state values. This needs to be addressed with code or an explicit timing analysis.
minor comments (6)
- [Section 2.1] The text says 'the UFC allows us to use only a 3-bit color encoder'; this should read 'UCF' (User Constraints File).
- [Section 3.4] The Verilog line 'assign direction = rotary_q2;' is not properly aligned or formatted, and the comment about 'counterclockwise or clockwise' should clarify the convention relative to ROTA/ROTB.
- [Section 4.1] The discussion of synthesis warnings ('I had quite a bit of warnings... due to a bit of lazy coding') is informal and does not identify which warnings appeared or why they were benign; a list of warning types would be more precise.
- [Figure 3] Figure 3 ('CRT Timing Example') is not referenced in the text and its source and relevance to the VGA timing discussion are unclear.
- [Section 4.3 / References] The Future Work section contains a large block of self-citations ([10]-[60]) that is not connected to specific technical claims in the paper; this reads as boilerplate and inflates the bibliography. It should be reduced to only the references actually used for specific statements.
- [Abstract / Section 1.1] The abstract and Section 1.1 repeat the same sentences nearly verbatim; the abstract should be a condensed, distinct summary.
Circularity Check
No circularity: the design is a constructed, hardware-validated implementation; self-citations in Future Work are not load-bearing.
full rationale
This paper is a design and implementation report, not a predictive derivation. The central claim that a Spartan-3E FPGA runs a washing machine FSM and renders a VGA HUD is supported by simulation testbenches, synthesis, and hardware demonstrations. The VGA timing follows VESA DMT specifications (with a 25 MHz pixel clock instead of 25.175 MHz, a correctness risk noted in Section 2.2, but not a circularity). The FSM states, debounce circuits, rotary encoder filter, and HUD color mapping are constructed components, not fitted parameters renamed as predictions. The only self-citations appear in Section 4.3 as an inspirational block asserting that FPGA-based systems are a good avenue for complex algorithms; this does not justify any specific design choice or claimed result in the paper. No equation reduces to its input by construction, no uniqueness theorem is imported from the authors, and no ansatz is smuggled in via citation. The derivation chain is self-contained and externally verified, so there is no significant circularity.
Assumptions & free parameters
free parameters (2)
- Load-dependent wash cycle durations =
not disclosed (small/medium/large)
- Timer counter scaling to real-world timing =
not specified
assumptions (3)
- domain assumption VGA monitors tolerate a 25 MHz pixel clock instead of the VESA standard 25.175 MHz.
- domain assumption Shift-register debouncing and the Xilinx rotary encoder synchronizer fully remove mechanical jitter.
- domain assumption The FSM's safety rule (door switch can only open during spin states) matches the intended safety behavior.
Cite this review
Pith. "Pith review of Design and Implementation of Washing Machine HUD Using FPGAs." pith.science (2026). https://pith.science/paper/CKT6ZYXC
@misc{pith2026250611287,
author = {Pith},
title = {Pith review of: Design and Implementation of Washing Machine HUD Using FPGAs},
year = {2026},
howpublished = {\url{https://pith.science/paper/CKT6ZYXC}},
note = {Machine review of arXiv:2506.11287}
}
read the original abstract
In contemporary digital design education, practical field programmable gate array (FPGA) projects are indispensable for bridging theoretical concepts with real-world applications. This project focuses on developing a hardware-based simulation of a domestic washing machine controller using the Xilinx Spartan-3E development board. A critical component of the design is the graphical heads-up display (HUD), which renders real-time information about the machine's operational state and cycle selections via a VGA interface.
Figures
Figures from the paper (1 more)
Reference graph
Works this paper leans on
-
[10]
Analysis of Single-Chip Hardware Support for Mobile and Embedded Applications,
D.G. Perera and K.F. Li, "Analysis of Single-Chip Hardware Support for Mobile and Embedded Applications," in Proc. of IEEE Pacific Rim Int. Conf. on Communication, Computers, and Signal Processing, (PacRim’13), pp. 369-376, Victoria, BC, Canada, August 2013
2013
-
[60]
Optimized Counter-Based Multi-Ported Memory Architectures for Next- Generation FPGAs
S.N. Shahrouzi and D.G. Perera, “Optimized Counter-Based Multi-Ported Memory Architectures for Next- Generation FPGAs”, in Proceedings of the 31st IEEE International Systems-On-Chip Conference, (SOCC’18), pp. 106-111, Arlington, VA, Sep. 2018
work page 2018
-
[1]
Perera, D. G. (2025). Rapid Prototyping with FPGAs Lecture slides [Lecture slides]. Department of Electrical & Computer Engineering, University of Colorado Colorado Springs
work page 2025
-
[2]
Xilinx, Inc., *Spartan-3E FPGA Family: Data Sheet*. Xilinx, 2006. [Online]. Available: Xilinx Documentation
work page 2006
-
[3]
VESA, VESA and Industry Standards and Guidelines for Computer Display Monitor Timing (DMT). VESA, 2000
work page 2000
-
[4]
Chapman, *Rotary Encoder Interface for Spartan-3E Starter Kit*
K. Chapman, *Rotary Encoder Interface for Spartan-3E Starter Kit*. Xilinx, 2006
work page 2006
-
[5]
Nandland, FPGA Programming with Verilog. 2021. [Online]. Available: https://www.nandland.com/
work page 2021
-
[6]
FPGA4Student, Two Ways to Load Text File to FPGA or Memory in Verilog. 2016. [Online]. Available: https://www.fpga4student.com/2016/11/two-ways-to-load-text-file-to-fpga-or.html
work page 2016
Show all 60 references
-
[7]
AMD/Xilinx Adaptive Support, *Display Image on Monitor Using Spartan- 3E*. 2020. [Online]. Available: https://adaptivesupport.amd.com/s/question/0D52E00006hpZajSAE/display- image-on-monitor-using-spartan3e
2020
-
[8]
EE Times, Design Recipes for FPGAs: A Simple VGA Interface . 2015. [Online]. Available: https://www.eetimes.com/design-recipes-for-fpgas-a-simple-vga-interface/
2015
-
[9]
ePanorama, VGA Timing Specifications . 2003. [Online]. Available: https://www.epanorama.net/documents/pc/vga_timing.html
2003
-
[11]
Analysis of Computation Models and Application Characteristics Suitable for Reconfigurable FPGAs
D.G. Perera and K.F. Li, “Analysis of Computation Models and Application Characteristics Suitable for Reconfigurable FPGAs”, in Proc. of 10th IEEE Int. Conf. on P2P, Parallel, Grid, Cloud, and Internet Computing, (3PGCIC’15), pp. 244-247, Krakow, Poland, Nov. 2015
2015
-
[12]
Optimized Hardware Accelerators for Data Mining Applications on Embedded Platform: Case Study Principal Component Analysis,
S.N. Shahrouzi and D.G. Perera, "Optimized Hardware Accelerators for Data Mining Applications on Embedded Platform: Case Study Principal Component Analysis," Elsevier Journal on Microprocessor and Microsystems (MICPRO), vol. 65, pp. 79-96, March 2019
2019
-
[13]
Embedded Hardware Solution for Principal Component Analysis,
D.G. Perera and K.F. Li, "Embedded Hardware Solution for Principal Component Analysis," in Proc. of IEEE Pacific Rim Int. Conf. on Communication, Computers, and Signal Processing, (PacRim’11), pp.730- 735, Victoria, BC, Canada, August 2011
2011
-
[14]
Hardware Acceleration for Similarity Computations of Feature Vectors,
D.G. Perera and Kin F. Li, “Hardware Acceleration for Similarity Computations of Feature Vectors,” IEEE Canadian Journal of Electrical and Computer Engineering, (CJECE), vol. 33, no. 1, pp. 21 -30, Winter 2008
2008
-
[15]
On-Chip Hardware Support for Similarity Measures,
D.G. Perera and K.F. Li, “On-Chip Hardware Support for Similarity Measures,” in Proc. of IEEE Pacific Rim Int. Conf. on Communication, Computers, and Signal Processing, (PacRim’07), pp. 354- 358, Victoria, BC, Canada, August 2007
2007
-
[16]
An Investigation of Chip-Level Hardware Support for Web Mining,
K.F. Li and D.G. Perera, “An Investigation of Chip-Level Hardware Support for Web Mining,” in Proc. of IEEE Int. Symp. on Data Mining and Information Retrieval, (DMIR’07), pp. 341- 348, Niagara Falls, ON, Canada, May 2007
2007
-
[17]
A Hardware Collective Intelligent Agent
K.F. Li and D.G. Perera, “A Hardware Collective Intelligent Agent”, Transactions on Computational Collective Intelligence, LNCS 7776, Springer, pp. 45-59, 2013
2013
-
[18]
Optimizing Density-Based Ant Colony Stream Clustering Using FPGA-Based Hardware Accelerator
J.R. Graf and D.G. Perera, “Optimizing Density-Based Ant Colony Stream Clustering Using FPGA-Based Hardware Accelerator”, in Proc. Of IEEE Int. Symp. on Circuits and Systems (ISCAS’23), 5 -page manuscript, Monterey, California, May 2023
2023
-
[19]
Chip-Level and Reconfigurable Hardware for Data Mining Applications,
D.G. Perera, “Chip-Level and Reconfigurable Hardware for Data Mining Applications,” PhD Dissertation, Department of Electrical & Computer Engineering, University of Victoria, Victoria, BC, Canada, April 2012
2012
-
[20]
Optimized Embedded and Reconfigurable Hardware Architectures and Techniques for Data Mining Applications on Mobile Devices
S. Navid Shahrouzi, "Optimized Embedded and Reconfigurable Hardware Architectures and Techniques for Data Mining Applications on Mobile Devices", PhD Dissertation, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, December 2018
2018
-
[21]
Optimizing Density-Based Ant Colony Stream Clustering Using FPGAs
J. Graf, "Optimizing Density-Based Ant Colony Stream Clustering Using FPGAs", MSc Thesis, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, CO, USA, March 2022
2022
-
[22]
Efficient Embedded Architectures for Model Predictive Controller for Battery Cell Management in Electric Vehicles
A.K. Madsen and D.G. Perera, “Efficient Embedded Architectures for Model Predictive Controller for Battery Cell Management in Electric Vehicles”, EURASIP Journal on Embedded Systems, SpringerOpen, vol. 2018, article no. 2, 36-page manuscript, July 2018. 14
2018
-
[23]
An Optimized FPGA-Based Hardware Accelerator for Physics- Based EKF for Battery Cell Management
A.K. Madsen, M.S. Trimboli, and D.G. Perera, “An Optimized FPGA-Based Hardware Accelerator for Physics- Based EKF for Battery Cell Management”, in Proc. of IEEE Int,l Symp, on Circuits and Systems, (ISCAS’20), 5-page manuscript, Seville, Spain, May 2020
2020
-
[24]
Towards Composing Efficient FPGA-Based Hardware Accelerators for Physics-Based Model Predictive Control Smart Sensor for HEV Battery Cell Management
A.K. Madsen and D.G. Perera, “Towards Composing Efficient FPGA-Based Hardware Accelerators for Physics-Based Model Predictive Control Smart Sensor for HEV Battery Cell Management”, IEEE ACCESS, (Open Access Journal in IEEE), pp. 106141-106171, 25th September 2023
2023
-
[25]
Composing Optimized Embedded Software Architectures for Physics -Based EKF-MPC Smart Sensor for Li-Ion Battery Cell Management
A.K. Madsen and D.G. Perera, “Composing Optimized Embedded Software Architectures for Physics -Based EKF-MPC Smart Sensor for Li-Ion Battery Cell Management”, Sensors, MDPI open access journal, Intelligent Sensors Section, 21-page manuscript, vol. 22, no. 17, 26th August 2022
2022
-
[26]
Optimized Embedded Architectures for Model Predictive Control Algorithms for Battery Cell Management Systems in Electric Vehicles
A.K. Madsen, “Optimized Embedded Architectures for Model Predictive Control Algorithms for Battery Cell Management Systems in Electric Vehicles”; PhD Dissertation, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, August 2020
2020
-
[27]
An FPGA-Based Linear Kalmann Filter for a Two-Phase Buck Converter Application
D. Abillar, "An FPGA-Based Linear Kalmann Filter for a Two-Phase Buck Converter Application", MSc Thesis, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, CO, USA, April 2024
2024
-
[28]
Efficient FPGA-Based Reconfigurable Accelerators for SIMON Cryptographic Algorithm on Embedded Platforms
A. Alkamil and D.G. Perera, “Efficient FPGA-Based Reconfigurable Accelerators for SIMON Cryptographic Algorithm on Embedded Platforms”, in Proceedings of the IEEE International Conferences on Reconfigurable Computing and FPGAs, (ReConFig’19), 8-page manuscript, Cancun, Mexico,...
2019
-
[29]
Towards Dynamic and Partial Reconfigurable Hardware Architectures for Cryptographic Algorithms on Embedded Devices
A. Alkamil and D.G. Perera, “Towards Dynamic and Partial Reconfigurable Hardware Architectures for Cryptographic Algorithms on Embedded Devices”, IEEE Access, Open Access Journal in IEEE, vol. 8, pp: 221720 – 221742, 10th December 2020
2020
-
[30]
Dynamic Reconfigurable Architectures to Improve Performance and Scalability of Cryptosystems on Embedded Systems
A. Alkamil, “Dynamic Reconfigurable Architectures to Improve Performance and Scalability of Cryptosystems on Embedded Systems”, PhD Dissertation, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, 5th February 2021
2021
-
[31]
An FPGA-Based Hardware Accelerator for K-Nearest Neighbor Classification for Machine Learning on Mobile Devices
M.A. Mohsin and D.G. Perera, “An FPGA-Based Hardware Accelerator for K-Nearest Neighbor Classification for Machine Learning on Mobile Devices”, in Proceedings of the IEEE/ACM International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, (HEART’18), ...
2018
-
[32]
An Efficient FPGA-Based Hardware Accelerator for Convex Optimization- Based SVM Classifier for Machine Learning on Embedded Platforms
S. Ramadurgam and D.G. Perera, “An Efficient FPGA-Based Hardware Accelerator for Convex Optimization- Based SVM Classifier for Machine Learning on Embedded Platforms”, Electronics, MDPI open access journal, 36-page manuscript, vol. 10, no. 11, 31st May 2021
2021
-
[33]
A Systolic Array Architecture for SVM Classifier for Machine Learning on Embedded Devices
S. Ramadurgam and D.G. Perera, “A Systolic Array Architecture for SVM Classifier for Machine Learning on Embedded Devices”, in Proc. of IEEE Int. Symp. on Circuits and Systems (ISCAS’23), 5- page manuscript, Monterey, California, May 2023
2023
-
[34]
FPGA-based Hardware Accelerator for Bottleneck Residual Blocks of MobileNetV2 Convolutional Neural Networks
Jordi P. Miró, Mokhles A. Mohsin, Arkan Alkamil and Darshika G. Perera, “FPGA-based Hardware Accelerator for Bottleneck Residual Blocks of MobileNetV2 Convolutional Neural Networks”, in Proceedings of the IEEE Mid-West Symposium on Circuits and Systems (MWCAS’25), 5-page manus...
2025
-
[35]
An FPGA-Based Hardware Accelerator for K-Nearest Neighbor Classification for Machine Learning
M. A. Mohsin, "An FPGA-Based Hardware Accelerator for K-Nearest Neighbor Classification for Machine Learning", MSc Thesis, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, CO, USA, December 2017
2017
-
[36]
Optimized Embedded Architectures and Techniques for Machine Learning Algorithms for On-Chip AI Acceleration
S. Ramadurgam, “Optimized Embedded Architectures and Techniques for Machine Learning Algorithms for On-Chip AI Acceleration”, PhD Dissertation, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, 12th February 2021
2021
-
[37]
FPGA-Based Accelerators for Convolutional Neural Networks on Embedded Devices
J. P. Miro, " FPGA-Based Accelerators for Convolutional Neural Networks on Embedded Devices", MSc Thesis, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, CO, USA, May 2020
2020
-
[38]
Intelligent Cognitive Radio Architecture Applying Machine Learning and Reconfigurability
J. Nurmi and D.G. Perera, “Intelligent Cognitive Radio Architecture Applying Machine Learning and Reconfigurability” in Proc. of IEEE Nordic Circuits and Systems (NorCAS'21) Conf., 6- page manuscript, Oslo, Norway, October 2021
2021
-
[39]
High-Level Synthesis Based FPGA Accelerator for GPS Signal Image Feature Extraction
Kevin Young and Darsika G. Perera, “High-Level Synthesis Based FPGA Accelerator for GPS Signal Image Feature Extraction”, in Proceedings of the IEEE Mid-West Symposium on Circuits and Systems (MWCAS’25), 5-page manuscript, Lansing MI, August 2025. 15
2025
-
[40]
Reconfigurable Architectures for Data Analytics on Next-Generation Edge-Computing Platforms
D.G. Perera, “Reconfigurable Architectures for Data Analytics on Next-Generation Edge-Computing Platforms”, Featured Article, IEEE Canadian Review, vol. 33, no. 1, Spring 2021. DOI: 10.1109/MICR.2021.3057144
2021
-
[41]
Composing Efficient Computational Models for Real -Time Processing on Next-Generation Edge-Computing Platforms
M.A. Mohsin, S.N. Shahrouzi, and D.G. Perera, “Composing Efficient Computational Models for Real -Time Processing on Next-Generation Edge-Computing Platforms” IEEE ACCESS, (Open Access Journal in IEEE), 30-page manuscript, 13th February 2024
2024
-
[42]
FPGA -Based Hardware Architecture for Sequence Alignment by Genetic Algorithm
Laura H. Garcia, Arkan Alkamil, Mokhles A. Mohsin, Johannes Menzel and Darshika G. Perera, “FPGA -Based Hardware Architecture for Sequence Alignment by Genetic Algorithm”, in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’25), 5-page manuscript,...
2025
-
[43]
An FPGA-Based Hardware Accelerator for Sequence Alignment by Genetic Algorithm
L. H. Garcia, "An FPGA-Based Hardware Accelerator for Sequence Alignment by Genetic Algorithm", MSc Thesis, Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, CO, USA, December 2019
2019
-
[44]
Neuromorphic Sentiment Analysis Using Spiking Neural Networks
R.K. Chunduri and D.G. Perera, “Neuromorphic Sentiment Analysis Using Spiking Neural Networks”, Sensors, MDPI open access journal, Sensing and Imaging Section, 24-page manuscript, vol. 23, no. 7701, 6th September 2023
2023
- [45]
-
[46]
A Fast and Scalable FPGA-Based Parallel Processing Architecture for K-Means Clustering for Big Data Analysis
R. Raghavan and D.G. Perera, “A Fast and Scalable FPGA-Based Parallel Processing Architecture for K-Means Clustering for Big Data Analysis”, in Proceedings of the IEEE Pacific Rim International Conference on Communications, Computers, and Signal Processing, (PacRim’17), pp. 1-...
2017
-
[47]
Parallel Computation of Similarity Measures Using an FPGA -Based Processor Array,
D.G. Perera and Kin F. Li, “Parallel Computation of Similarity Measures Using an FPGA -Based Processor Array,” in Proceedings of 22nd IEEE International Conference on Advanced Information Networking and Applications, (AINA’08), pp. 955-962, Okinawa, Japan, March 2008
2008
-
[48]
A Fast and Scalable Hardware Architecture for K-Means Clustering for Big Data Analysis
R. Raghavan, "A Fast and Scalable Hardware Architecture for K-Means Clustering for Big Data Analysis", MSc Thesis, (Supervisor Dr. Darshika G. Perera), Department of Electrical & Computer Engineering, University of Colorado Colorado Springs, CO, USA, May 2016
2016
-
[49]
A Design Methodology for Mobile and Embedded Applications on FPGA -Based Dynamic Reconfigurable Hardware
D.G. Perera and K.F. Li, “A Design Methodology for Mobile and Embedded Applications on FPGA -Based Dynamic Reconfigurable Hardware”, International Journal of Embedded Systems, (IJES), Inderscience publishers, 23-page manuscript, vol. 11, no. 5, Sept. 2019
2019
-
[50]
Analysis of FPGA-Based Reconfiguration Methods for Mobile and Embedded Applications
D.G. Perera, “Analysis of FPGA-Based Reconfiguration Methods for Mobile and Embedded Applications”, in Proceedings of 12th ACM FPGAWorld International Conference, (FPGAWorld’15), pp. 15- 20, Stockholm, Sweden, September 2015
2015
-
[51]
Discrepancy in Execution Time: Static Vs. Dynamic Reconfigurable Hardware
D.G. Perera and K.F. Li, “Discrepancy in Execution Time: Static Vs. Dynamic Reconfigurable Hardware”, IEEE Pacific Rim Int. Conf. on Communications, Computers, and Signal Processing, (PacRim’24), 6- page manuscript, Victoria, BC, Canada, August 2024
2024
-
[52]
FPGA-Based Reconfigurable Hardware for Compute Intensive Data Mining Applications
D.G. Perera and Kin F. Li, “FPGA-Based Reconfigurable Hardware for Compute Intensive Data Mining Applications”, in Proc. of 6th IEEE Int. Conf. on P2P, Parallel, Grid, Cloud, and Internet Computing, (3PGCIC’11), pp. 100-108, Barcelona, Spain, October 2011
2011
-
[53]
Similarity Computation Using Reconfigurable Embedded Hardware,
D.G. Perera and Kin F. Li, “Similarity Computation Using Reconfigurable Embedded Hardware,” in Proceedings of 8th IEEE International Conference on Dependable, Autonomic, and Secure Computing (DASC’09), pp. 323-329, Chengdu, China, December 2009
2009
-
[54]
Dynamic Partial Reconfigurable Hardware Architecture for Principal Component Analysis on Mobile and Embedded Devices
S.N. Shahrouzi and D.G. Perera, “Dynamic Partial Reconfigurable Hardware Architecture for Principal Component Analysis on Mobile and Embedded Devices”, EURASIP Journal on Embedded Systems, SpringerOpen, vol. 2017, article no. 25, 18-page manuscript, 21st February 2017
2017
-
[55]
HDL Code Optimization: Impact on Hardware Implementations and CAD Tools
S.N Shahrouzi and D.G. Perera, “HDL Code Optimization: Impact on Hardware Implementations and CAD Tools”, in Proc. of IEEE Pacific Rim Int. Conf. on Communications, Computers, and Signal Processing, (PacRim’19), 9-page manuscript, Victoria, BC, Canada, August 2019
2019
-
[56]
HDL Code Variation: Impact on FPGA Performance Metrics and CAD Tools
I.D. Atwell and D.G. Perera, “HDL Code Variation: Impact on FPGA Performance Metrics and CAD Tools”, IEEE Pacific Rim Int. Conf. on Communications, Computers, and Signal Processing, (PacRim’24), 6- page manuscript, Victoria, BC, Canada, August 2024. 16
2024
-
[57]
Towards Composing Optimized Bi -Directional Multi-Ported Memories for Next-Generation FPGAs
S.N. Shahrouzi, A. Alkamil, and D.G. Perera, “Towards Composing Optimized Bi -Directional Multi-Ported Memories for Next-Generation FPGAs”, IEEE Access, Open Access Journal in IEEE, vol. 8, no. 1, pp. 91531- 91545, 14th May 2020
2020
-
[58]
An Efficient Embedded Multi-Ported Memory Architecture for Next- Generation FPGAs
S.N. Shahrouzi and D.G. Perera, “An Efficient Embedded Multi-Ported Memory Architecture for Next- Generation FPGAs”, in Proceedings of 28th Annual IEEE International Conferences on Application- Specific Systems, Architectures, and Processors, (ASAP’17), pp. 83-90, Seattle, WA,...
2017
-
[59]
An Efficient FPGA-Based Memory Architecture for Compute-Intensive Applications on Embedded Devices
S.N. Shahrouzi and D.G. Perera, “An Efficient FPGA-Based Memory Architecture for Compute-Intensive Applications on Embedded Devices”, in Proceedings of the IEEE Pacific Rim International Conference on Communications, Computers, and Signal Processing, (PacRim’17), pp. 1- 8, Vic...
2017
Reviewed August 7, 2026 · model on record in the stance chip above.
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