An Automatic AW-SOM VHDL IP-core Generator
In this paper, the authors present a MATLAB IP generator for hardware accelerators of All-Winner Self-Organizing Maps (AW-SOM). AW-SOM is a modified version of Kohonen’s Self Organizing Maps (SOM) algorithm, which is one of the most used Machine Learning algorithms for data clustering, and vector quantization. The architecture of the AW-SOM method is meant for hardware implementations, and its main feature is a processing speed almost independent to the number of neurons since each of them is processed in a parallel way; the parallelization can be easily exploited by hardware custom hardware designs. The IP generator is built-in MATLAB and provides the user with the possibility to design a custom and efficient hardware accelerator. Several settings can be set such as the number of features and the number of neurons. The target language is the VHSIC Hardware Description Language (VHDL). The generated IP cores can be used for the training of the model and a built-in function of the software can also check the clustering performances using its inference capabilities. The accelerators produced by the software have been also characterized in terms of max frequency, hardware resources, and power consumption. The authors performed the hardware implementations on a XILINX Virtex 7 xc7vx690t FPGA.
G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, and M. Re, “TDES cryptography algorithm acceleration using a reconfigurable functional unit” in 21st IEEE International Conference on Electronics, Circuits and Systems, ICECS 2014, 2014, paper 7050011, pp. 419-422.
G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, S. Pontarelli, M. Re, and A. Salsano, “Implementation of the AES algorithm using a Reconfigurable Functional Unit” in ISSCS - International Symposium on Signals, Circuits and Systems, Proceedings, 2011, paper 5978668, pp. 97-100.
F. Silvestri, S. Acciarito, G.C. Cardarilli, G.M. Khanal, L. Di Nunzio, R. Fazzolari, and M. Re, “FPGA implementation of a low-power QRS extractor” in Lecture Notes in Electrical Engineering, 2019, paper 512, pp. 9-15.
R. Ammendola and P. Loreti, “Design and evaluation of a scalable engine for 3D-FFT computation in an FPGA cluster”, International Journal on Advanced Science, Engineering and Information Technology, vol. 9 (2), pp. 677-684, 2019.
F. Silvestri, S. Acciarito, and G.M. Khanal, “Relationship between mathematical parameters of modified Van der Pol Oscillator model and ECG morphological features”, International Journal on Advanced Science, Engineering and Information Technology, vol. 9 (2), pp. 601-608, 2019.
Andrizal, R. Chadry, and A.I. Suryani, “Embedded System Using Field Programmable Gate Array (FPGA) myRIO and LabVIEW Programming to Obtain Data Patern Emission of Car Engine Combustion Categories”, JOIV : International Journal on Informatics Visualization, vol. 2 (2), 2018...
A.R. Kardian, S.A. Sudiro, and S. Madenda, “Efficient implementation of mean, variance and skewness statistic formula for image processing using FPGA device”, Bulletin of Electrical Engineering and Informatics, vol. 7 (3), pp. 386-392, 2018.
Iswanto, O. Wahyunggoro, and A.I. Cahyadi, “Formation pattern based on modified cell decomposition algorithm”, International Journal on Advanced Science, Engineering and Information Technology, vol. 7 (3), pp. 829-835, 2017.
Andrizal, B. Bakhtiar, and R. Chadry, “Detection combustion data pattern on gasoline fuel motorcycle with carburetor system”, International Journal on Advanced Science, Engineering and Information Technology, vol. 6 (1), pp. 107-111, 2016.
A.O. Mulani and P.B. Mane, “Watermarking and cryptography based image authentication on reconfigurable platform”, Bulletin of Electrical Engineering and Informatics, vol. 6 (2), pp. 181-187, 2017.
G. Capizzi, G. and G. Tina. "Long-term operation optimization of integrated generation systems by fuzzy logic-based management", Energy, vol. 32 (7), pp. 1047-1054 , 2007.
G. Capizzi, G. Lo Sciuto, P. Monforte, and C. Napoli, "Cascade feed forward neural network-based model for air pollutants evaluation of single monitoring stations in urban areas.", International Journal of Electronics and Telecommunications, vol. 61 (4), pp. 327-332, 2015.
P. Loreti, L. Bracciale, and A. Caponi, "Push Attack: Binding Virtual and Real Identities Using Mobile Push Notifications", Future Internet, vol. 10 (2), p. 13, 2018.
F. Beritelli, G. Capizzi, G. Lo Sciuto, C. Napoli, and F. Scaglione, "Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks", Biomedical engineering letters, vol. 8 (1), pp. 77-85, 2018.
M. Matta, G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Re, F. Silvestri, and S. Spanò, “Q-RTS: a real-time swarm intelligence based on multi-agent Q-learning”, Electronics Letters, vol. 55 (10), pp. 589-591, 2019.
L. Bracciale, P. Loreti, and G. Bianchi. "The sleepy bird catches more worms: revisiting energy efficient neighbor discovery", IEEE Transactions on Mobile Computing, vol. 15 (7), pp. 1812-1825, 2015.
D. Giardino, M. Matta, F: Silvestri, S. Spanò, and V. Trobiani, “FPGA Implementation of Hand-written Number Recognition Based on CNN”, International Journal on Advanced Science, Engineering and Information Technology, vol. 9 (1), pp. 167-171, 2019.
G.C. Cardarilli, R. Fazzolari, D. Giardino, M. Matta, M. Re, F. Silvestri, and S. Spanò, “Efficient Ensemble Machine Learning Implementation on FPGA Using Partial Reconfiguration”, in International Conference on Applications in Electronics Pervading Industry, Environment and Society, 2018. pp. 253-259.
G. Susi, L.A. Toro, L. Canuet, M.E. López, F. Maestú, C.R. Mirasso, and E. Pereda, “A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency, and heterosynaptic STDP”, Frontiers in Neuroscience, vol. 12, 2018.
M. Salerno, G. Susi, and A. Cristini, “Accurate latency characterization for very large asynchronous spiking neural networks” in BIOINFORMATICS 2011 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, 2011, pp. 116-124.
G. Susi, A. Cristini, and M. Salerno, “Path multimodality in a feedforward snn module, using lif with latency model”, Neural Network World, vol. 26 (4), pp. 363-376, 2016.
A. Detti, M. Orru, R. Paolillo, G. Rossi, P. Loreti, L. Bracciale, and N. Blefari Melazzi, "Application of information centric networking to NoSQL databases: the spatio-temporal use case", in 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2017, pp. 1-6.
P. Loreti, A. Catini, M. De Luca, L. Bracciale, G. Gentile, and C. Di Natale, “The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas”, Sensors, vol. 19 (5), p. 985, 2019.
L. Bracciale, A. Catini, G. Gentile, and P. Loreti, “Delay tolerant wireless sensor network for animal monitoring: The Pink Iguana case”, in Lecture Notes in Electrical Engineering, 2017, paper 429, pp. 18-26.
G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, M. Re, and S. Spanò, “AW-SOM, an Algorithm for High-speed Learning in Hardware Self-Organizing Maps”, IEEE Transactions on Circuits and Systems II: Express Briefs, 2019.
T. Kohonen, “The self-organizing map”, in Proceedings of the IEEE, 1990, vol. 78 (9), pp. 1464-1480.
D. Giardino, M. Matta, M. Re, F. Silvestri, and S. Spanò, “IP Generator Tool for Efficient Hardware Acceleration of Self-organizing Maps”, in International Conference on Applications in Electronics Pervading Industry, Environment and Society, 2018. pp. 493-499.
E. De Luca, F. Fallucchi, R, Giuliano, G. Incarnato, and F. Mazzenga, “Analysing and visualizing tweets for U.S. president popularity”, International Journal on Advanced Science, Engineering and Information Technology, vol. 9 (2), pp. 692-699, 2019
A.K. Dubey, U. Gupta, and S. Jain, “Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data”, International Journal on Advanced Science, Engineering and Information Technology, vol. 8 (1), pp. 18-29, 2018.
M.F.A. Saputra, T. Widiyaningtyas, and A.P. Wibawa, “Illiteracy Classification Using K Means-Naïve Bayes Algorithm”, JOIV: International Journal on Informatics Visualization, vol. 2 (3), 2018.
H.M. Rahman, N. Arbaiy, M. S. Che Lah, and N. Hassan, “Exploratory Study of Kohonen Network for Human Health State Classification”, JOIV : International Journal on Informatics Visualization, vol. 2 (3), 2018.
L.C. Lee, C.Y. Liong, and A.A. Jemain, “Applying fourier-transform infrared spectroscopy and self-organizing maps for forensic classification of white-copy papers”, International Journal on Advanced Science, Engineering and Information Technology, vol. 6 (6), pp. 1033-1039, 2016.
A.P. Rahmadini, P. Kristalina, and A. Sudarsono, “Optimization of fingerprint indoor localization system for multiple object tracking based on iterated weighting constant - KNN method”, International Journal on Advanced Science, Engineering and Information Technology, 8 (3), vol. pp. 998-1007, 2018.
H. Hikawa and Y. Maeda: “Improved Learning Performance of Hardware Self-Organizing Map Using a Novel Neighborhood Function”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26 (11), pp. 2861-2873, 2015.
M.A De Abreu De Sousa and E. Del-Moral-Hernandez, “Comparison of three FPGA architectures for embedded multidimensional categorization through Kohonen’s self-organizing maps”, in Proceedings – IEEE International Symposium on Circuits and Systems, 2017.
G. Iazeolla and A. Pieroni, “Power management of server farms”, Applied Mechanics and Materials, pp. 453-459, 2014
Ricci, E., Cianca, E., Rossi, T., Diomedi, M., & Deshpande, P. “Performance evaluation of novel microwave imaging algorithms for stroke detection using an accurate 3D head model”. Wireless Personal Communications, 96(3), 3317-3331.
Alsayat, A., El-Sayed, H. “Efficient genetic K-Means clustering for health care knowledge discovery” 2016 IEEE/ACIS 14th International Conference on Software Engineering Research, Management and Applications, SERA 2016, art. no. 7516127,
Quitadamo, L.R., Abbafati, M., Cardarilli, G.C., Mattia, D., Cincotti, F., Babiloni, F., Marciani, M.G., Bianchi, L. “Evaluation of the performances of different P300 based brain-computer interfaces by means of the efficiency metric” (2012) Journal of Neuroscience Methods, 203 (2), pp. 361-368.
Gao, W., Qian, G., Xu, H. “SOM clustering analysis for telecommunication customer segmentation” (2009) Proceedings - International Conference on Management and Service Science, MASS 2009, art. no. 5301514,
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