Modification of Sigma-Delta DAC for Digital Spike Signal Processing
DOI:
https://doi.org/10.46962/snte.25.096Abstract
his study aims to modify the architecture of a Sigma-Delta Digital-to-Analog Converter (DAC) based on FPGA to support the conversion of digital spike signals to analog in neuromorphic systems. The main focus of the modification lies in increasing the order of the noise loop filter and implementing a Multi-Stage Noise Shaping (MASH) structure consisting of a combination of 1-bit and 3-bit DACs. The modification process was carried out in the truncation and feedback stages and simulated using the Verilog programming language on the Altera Cyclone V FPGA. Evaluation was conducted using tonic spike inputs representing a 15 Hz sinusoidal signal. The results show improvements in the linearity of the analog output signal, enhancement of quality parameters such as SNR and ENOB, and reduced latency. Nevertheless, some challenges remain related to truncation errors that have not been fully addressed.
References
Yang, S., Liu, P., Xue, J., Sun, R., & Ying, R. (2020, October). An efficient fpga implementation of Izhikevich neuron model. In 2020 International SoC Design Conference (ISOCC) (pp. 141-142). IEEE.
Antoniuk, V. V., Drozd, M. O., & Drozd, O. B. (2019). Power-oriented checkability and monitoring of the current consumption in FPGA projects of the critical applications. Applied Aspects of Information Technology, 2(2), 105-114.
Sass, R., & Schmidt, A. G. (2010). Embedded systems design with platform FPGAs: principles and practices.
Morgan Kaufmann.
Rahman, L. F., Rudham, F. A., Reaz, M. B. I., & Marufuzzaman, M. (2016, November). The evolution of digital to analog converter. In 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES) (pp. 151-154). IEEE.
Pujari, S. S., Muduli, P. P., Panda, A., Badhai, R., Nayak, S., & Sahoo, Y. (2014, February). Design & implementation of FIR filters using on- board ADC-DAC & FPGA. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-6). IEEE.
James, R., Garside, J., Plana, L. A., Rowley, A., & Furber, S. B. (2018). Parallel distribution of an inner hair cell and auditory nerve model for real-time application. IEEE transactions on biomedical circuits and systems, 12(5), 1018-1026.
Haessig, G., Galluppi, F., Lagorce, X., & Benosman, R. (2019, March). Neuromorphic networks on the SpiNNaker platform. In 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) (pp. 86-91). IEEE.
Prashanth, B. U. V., & Ahmed, M. R. (2020, January). FPGA Implementation of bio-inspired computing architecture based on simple neuron model. In 2020 International Conference on Artificial Intelligence and Signal Processing (AISP) (pp. 1-6). IEEE.
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S., Hudspeth, A. J., & Mack, S. (Eds.). (2000). Principles of neural science (Vol. 4, pp. 1227-1246). New York: McGraw-hill.
Young, A. R., Dean, M. E., Plank, J. S., & Rose, G. S. (2019). A review of spiking neuromorphic hardware communication systems. IEEE Access, 7, 135606-135620.
C. Zet and C. Fosalau, "Generating Programmable Analog Signals using FPGA," 2019 International Conference on Electromechanical and Energy Systems (SIELMEN), Craiova, Romania, 2019, pp. 1-4, doi: 10.1109/SIELMEN.2019.8905870.
O. M. Chacón, J. Wikner, A. Alvandpour and L. Siek, "A 10-bit 3.75-GS/s Binary-Weighted DAC with 58.6-pJ Energy Consumption in 65-nm CMOS," 2020 IEEE Nordic Circuits and Systems Conference (NorCAS), Oslo, Norway, 2020, pp. 1-4, doi: 10.1109/NorCAS51424.2020.9265003.
Sass, R., & Schmidt, A. G. (2010). Embedded Systems Design with platform fpgas: Principles and practices. Morgan Kaufmann.
B. U. V. Prashanth and M. R. Ahmed, "FPGA Implementation of Bio-Inspired Computing Architecture Based on Simple Neuron Model," 2020 International Conference on Artificial Intelligence and Signal Processing (AISP), Amaravati, India, 2020, pp. 1-6, doi: 10.1109/AISP48273.2020.9073420.
M. T. Islam, F. Hazzazi, A. Hoque, S. Haghiri, M. A. Chaudhary and M. Ghanbarpour, "FPGA Implementation of Nerve Cell Using Izhikevich Neuronal Model as Spike Generator (SG)," in IEEE Access, vol. 12, pp. 2303-2312, 2024, doi: 10.1109/ACCESS.2023.3343156.
Kungl, Á. F. (2020). Robust learning algorithms for spiking and rate-based neural networks (Doctoral dissertation).
Uenohara, S., & Aihara, K. (2021). Time-domain digital-to-analog converter for spiking neural network hardware. Circuits, Systems, and Signal Processing, 40(6), 2763-2781
Nguyen, D. A., Tran, X. T., & Iacopi, F. (2021). A review of algorithms and hardware implementations for spiking neural networks. Journal of Low Power Electronics and Applications, 11(2), 23.
R. Massa, A. Marchisio, M. Martina and M. Shafique, "An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-9, doi: 10.1109/IJCNN48605.2020.9207109.
F. Akopyan et al., "TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 34, no. 10, pp. 1537-1557, Oct. 2015, doi: 10.1109/TCAD.2015.2474396.
Intel Labs. (2021). Taking neuromorphic computing to the next level with Loihi 2: Technology brief.
Intel Corporation. https://www.intel.com/content/www/us/en/research/neuromorp hic-community.html
Urry, L. A., Cain, M. L., Wasserman, S. A., Minorsky, P. V., Orr, R. B., & Campbell, N. A. (2021). Campbell Biology. Pearson.
G. Ghanbarpour, M. Assaad and M. Ghanbarpour, "New Model For Wilson and Morris–Lecar Neuron Models: Validation and Digital Implementation on FPGA," in IEEE Access, vol. 12, pp. 154751-154759, 2024, doi: 10.1109/ACCESS.2024.3417613.
Chéron, G., Prigogine, C., Cheron, J., Marquez-Ruiz, J., Traub, R. D., & Dan, B. (2014). Emergence of a 600- Hz buzz UP state Purkinje cell firing in alert mice. Neuroscience, 263, 15-26.
de la Rosa, J. M., Schreier, R., Pun, K. P., & Pavan, S. (2015). Next-generation delta-sigma converters: Trends and perspectives. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 5(4), 484-499.
Alkabaa, A. S., Taylan, O., Yilmaz, M. T., Nazemi, E., & Kalmoun, E. M. (2022). An investigation on spiking neural networks based on the izhikevich neuronal model: Spiking processing and hardware approach. Mathematics, 10(4), 612.
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press.
Pavan, S., Schreier, R., & Temes, G. C. (2017). Understanding delta-sigma data converters. John Wiley & Sons. Kester, W. (2005). Data conversion handbook. Newnes.
Terasic Technologies. (2014). Cyclone V GX Starter Kit User Manual. Terasic Technologies. Diakses dari http://www.terasic.com/
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Seminar Nasional Teknik Elektro

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


