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Characterization of a Spiking Convolutional Processor for FPGA.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § 3. Methodology and Experimental Design › 3.2. Validation of the Spiking Approach ↔ MatLab_Scripts/metrics.m, the whole file · a weak match · score 0.74 · Kullback Leibler, Jensen Shannon, JS, probability
  2. [2] § 3. Methodology and Experimental Design › 3.2. Validation of the Spiking Approach ↔ MatLab_Scripts/metrics.m, the whole file · a weak match · score 0.64 · Kullback Leibler, Jensen Shannon, metrics
  3. [3] § 3. Methodology and Experimental Design › 3.1. Configuration Parameters ↔ MatLab_Scripts/conv2_processing.m, the whole file · a weak match · score 0.59 · horizontal edges, Sobel kernels, vertical
  4. [4] § 3. Methodology and Experimental Design ↔ Config_Sources/weights_parser_SCNN.py, lines 22–106 · score 0.51 · convolution engine, convolution kernel, row, refractory

Paper

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The authors' code

MATLAB · 79 lines · 2 KB · GPL-3.0 · 2 matches

  2. dim = 128;
  3. dim_kernel = 5;
  4. num_th = 11;
  5. num_sc = 10;
  6. pad = (dim_kernel-1)/2;
  8. mp = zeros(num_th, num_sc);
  9. mp1 = zeros(num_th, num_sc);
  10. cn_act = zeros(num_th, num_sc);
  11. cn_no_act = zeros(num_th, num_sc);
  12. Jaccard = zeros(num_th, num_sc);
  13. KL = zeros(num_th, num_sc);
  14. JS = zeros(num_th, num_sc);
  15. RMSE = zeros(num_th,num_sc);
  16. RMSE_norm = zeros(num_th,num_sc);
  18. [TH,SC] = meshgrid(50:5:100,1:num_sc);
  20. for th = 1:num_th
  22. for sc = 1:num_sc
  24. A = reshape(spkconv_ver_H1(th,sc,:,:),[dim,dim]);
  25. B = reshape(Cv_H1(sc,:,:),[dim,dim]);
  27. RMSE(th,sc) = sqrt(mean((A(:) - B(:)).^2));
  29. RMSE_norm(th,sc) = RMSE(th,sc) / max(A(:)); % Normalized RMSE (relative to max value)
  30. %RMSE_norm = RMSE / (max(A(:)) - min(A(:))); % relative to dinamic range
  32. scnn = reshape(double(spkconv_ver_H1(th,sc,:,:) > 0),[dim,dim]);
  33. matl = reshape(double(Cv_H1(sc,:,:) > 5e-04),[dim,dim]);
  35. cn = double(scnn == matl);
  36. mp(th,sc) = sum(cn(:)) / numel(cn);
  38. % Kullback-Leibler
  39. eps_val = 1e-12;
  41. scnn_prob = A + eps_val;
  42. matl_prob = B + eps_val;
  44. scnn_prob = scnn_prob / sum(scnn_prob);
  45. matl_prob = matl_prob / sum(matl_prob);
  47. KL(th,sc) = sum(scnn_prob .* log(scnn_prob ./ matl_prob));
  49. % Jensen-Shannon
  50. M = 0.5*(scnn_prob + matl_prob);
  51. JS(th,sc) = 0.5*sum(scnn_prob.*log(scnn_prob./M)) + 0.5*sum(matl_prob.*log(matl_prob./M));
  53. end
  55. end
  57. figure(1);
  58. surf(TH,SC,mp');
  59. title('MP - threshold, scale');
  60. xlabel('threshold');
  61. ylabel('scale');
  63. figure(2);
  64. surf(TH,SC,Jaccard');
  65. title('Jaccard - threshold, scale');
  66. xlabel('threshold');
  67. ylabel('scale');
  69. figure(3);
  70. surf(TH,SC,JS');
  71. title('Jensen-Shannon - threshold, scale');
  72. xlabel('threshold');
  73. ylabel('scale');
  75. figure(4);
  76. surf(TH,SC,RMSE');
  77. title('RMSE - threshold, scale');
  78. xlabel('threshold');
  79. ylabel('scale');

metrics.m at commit c740edf, under GPL-3.0 · at the source

Overview

Authors: Dagnier A Curra-Sosa1, Francisco Gomez-Rodriguez1, Alejandro Linares-Barranco1
ORCID iDs: Dagnier A Curra-Sosa, Francisco Gomez-Rodriguez, Alejandro Linares-Barranco
  1. Neuromorphic Engineering Group of SCORE Excellence Unit (I3US), Department of Computer Architecture and Technology, EPS-ETSII, Universidad de Sevilla, 41004 Sevilla, Spain; (F.G.-R.); (A.L.-B.)
Institutions: Universidad de Sevilla (Spain)
Journal: Sensors (Basel, Switzerland), volume 26, issue 6, article 1801
Dates: received 30 December 2025; accepted 23 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/s26061801 · PMID 41901971 · PMCID PMC13029829 · OpenAlex W7135074424
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: methods / tools (subfield)
Methods: Machine learning
Keywords: FPGA, DVS, Address-Event-Representation (AER), Spiking Convolution Neural Network (SCNN), LIF neuron model
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: Junta de Andalucía (PAIDI 2020 QUAL21 008 USE); NEKOR (ID2023-149071NB-C54)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

In event-based neuromorphic processing, computer vision finds an efficient alternative capable of optimizing computational and energy resources, inspired by the dynamics of biological neural systems. In the development of real-time processing systems, it is crucial to visually represent the information captured by sensors and to explore its content with precision. Thus, machine learning models are implemented with the capability of being deployed on hardware devices with limited capabilities, depending on the intended purpose, ensuring savings in computational resources. The aim of this work was to evaluate the limits of the implemented neuron model, leaky-integrate and fire (LIF), for fitting convolutional layers of a neural network. To this end, the characteristics of the LIF neuron model used are summarized, as well as the details of its implementation in a hardware design, using configurable parameters. The experimental phase considered two convolution approaches to compare performance, Matlab R2022a software and a spiking convolutional processor for an FPGA, using sample recordings from the MNIST-DVS dataset and Sobel kernels for edge detection. The results reflect that the number of spikes generated by both approaches is very similar and their distribution by frame addresses is directly proportional.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

RTC-research-group/SCNN_LIFrow

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: c740edfaf69cd45ab5d3b7b7504288e47089fda2, 14 February 2026
Languages: MATLAB (14), C++ (10), Python (1)
Size: 63 files, 25 scripts
Software Heritage: not archived
Found in: the text, “2.6. Hardware Implementation”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
27 files
At the source: github.com/RTC-research-group/SCNN_LIFrow

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 25 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The original data presented in the study are openly available in http://imse-cnm.csic.es/caviar/MNIST_DVS accessed on 20 February 2026.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 2 funders, 43 references.

Cite

This paper

Curra-Sosa, D. A., Gomez-Rodriguez, F., & Linares-Barranco, A. (2026). Characterization of a Spiking Convolutional Processor for FPGA. Sensors (Basel, Switzerland), 26(6), 1801. https://doi.org/10.3390/s26061801

BibTeX

@article{currasosa2026characterization,
author = {Curra-Sosa, Dagnier A and Gomez-Rodriguez, Francisco and Linares-Barranco, Alejandro},
title = {{Characterization of a Spiking Convolutional Processor for FPGA}},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
month = mar,
volume = {26},
number = {6},
pages = {1801},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1424-8220},
doi = {10.3390/s26061801},
url = {https://doi.org/10.3390/s26061801},
pmid = {41901971},
pmcid = {PMC13029829}
}

RIS

TY - JOUR
AU - Curra-Sosa, Dagnier A
AU - Gomez-Rodriguez, Francisco
AU - Linares-Barranco, Alejandro
TI - Characterization of a Spiking Convolutional Processor for FPGA
T2 - Sensors (Basel, Switzerland)
J2 - Sensors (Basel)
PY - 2026
DA - 2026/03/12
VL - 26
IS - 6
SP - 1801
SN - 1424-8220
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/s26061801
UR - https://doi.org/10.3390/s26061801
LA - en
ER -

CSL-JSON

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"container-title-short": "Sensors (Basel)",
"volume": "26",
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"PMCID": "PMC13029829",
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