DESIGNING OF HIGH-PERFORMANCE DIGITAL FILTERS USING THE BALANCED RESIDUE NUMBER SYSTEM MODULI SET

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Abstract

In this paper, we propose an approach for finding balanced special-type sets of moduli for high-speed digital signal filtering using the residue number system and utilizing the truncated multiply-accumulate blocks. The primary focus is to conduct a thorough analysis of the computational delay associated with individual residue number system moduli sets as well as their various combinations. Our proposed approach reduces the digital filtering delay by 60.85% depending on the computational range and bit-width of residue number system moduli for 16-, 24-, 32- and 48-bit filters. The proposed approach can significantly enhance the performance of digital devices where data processing speed is critical. Further research will explore applying these filters to tackle various challenges in both digital signal and image processing.

About the authors

P. A Lyakhov

North-Caucasus Federal University; North-Caucasus Center for Mathematical Research, North-Caucasus Federal University

Email: ljahov@mail.ru
ORCID iD: 0000-0003-0487-4779
Stavropol, Russia; Stavropol, Russia

N. N Nagornov

North-Caucasus Federal University

Email: sparta1392@mail.ru
ORCID iD: 0000-0002-9423-3555
Stavropol, Russia

M. V Bergerman

North-Caucasus Center for Mathematical Research, North-Caucasus Federal University

Email: maxx07051997@inbox.ru
ORCID iD: 0000-0003-2937-8501
Stavropol, Russia

A. S Abdulsalymova

North-Caucasus Center for Mathematical Research, North-Caucasus Federal University

Email: a.abdulsalyamova@mail.ru
ORCID iD: 0000-0002-3589-4257
Stavropol, Russia

R. I Abdulkadirov

North-Caucasus Center for Mathematical Research, North-Caucasus Federal University

Email: ruslanabdulkadirovstavropol@gmail.com
ORCID iD: 0000-0001-7792-1666
Stavropol, Russia

References

  1. Bhaskar P.C., Uplane M.D. FPGA based digital FIR multilevel filtering for ECG denoising, in Proc. Int. Conf. Inf. Process. (ICIP), 2015. P. 733–738.
  2. Kurbiel T., Göckler H.G., Alfsmann D. Oversampling complex-modulated digital filter bank pairs suitable for extensive subband-signal amplification, 2009 17th Eur. Signal Process. Conf., Glasgow, U.K., 2009. P. 2658- 2662.
  3. Porshnev S.V., Kusaykin D.V., Klevakin M.A. On accuracy of periodic discrete finite-length signal reconstruction by means of a Whittaker- Kotelnikov- Shannon interpolation formula, in Proc. Ural Symp. Biomed. Eng., Radioelectronics Inf. Technol. (US- BEREIT), 2018. P. 165- 168.
  4. Tang F., Wang Z., Xia Y., Liu F., Zhou X., Hu S., Lin Z., Bermak A. An area- efficient column- parallel digital decimation filter with pre- BWI topology for CMOS image sensor, in IEEE Trans. Circuits Syst. I, Reg. Papers. 2018. V. 65. No 8. P. 2524- 2533.
  5. Kiran S., Shafik A., Tabasy E.Z., Cai S., Lee K., Hoyos S., Palermo S. Modeling of ADC- based serial link receivers with embedded and digital equalization, in IEEE Trans. Compon., Packag., Manuf. Technol. 2019. V. 9. No 3. P. 536- 548.
  6. Chandra A., Chattopadhyay S. Design of hardware efficient FIR filter: A review of the state- of- the- art approaches, in Engineering Science and Technology, an International Journal. 2016. V. 19. No 1. P. 212- 226.
  7. Cunfu H., Sen W., Qiang W., Zenghua L., Bin W. Application of Signal Denoising by Using FIR Filter Based on FPGA in an Ultrasonic Guided Waves Receiving System, in Journal of Beijing University of Technology. 2018. V. 44. No 5. P. 658.
  8. Fedorenko V.A., Sorokina K.O., Giverts P.V. Multigroup Classification of Firing Pin Impressions with the Use of a Fully Connected Neural Network. Program Comput Soft. 2024. V. 50. P. 73- 84.
  9. Pak J.M. Hybrid PDA/FIR Filtering for Preceding Vehicle Tracking Using Automotive Radars, in IEEE Access. 2021. V. 9. P. 118726- 118735.
  10. Wijesekara R.T., Edussooriya C.U.S., Bruton L.T., Agathoklis P. A 3- D Sparse FIR Frustum Filter for Enhancing Broadband Plane Waves, in IEEE Transactions on Circuits and Systems II: Express Briefs. 2019. V. 66. No 3. P. 497- 501.
  11. Morales-Sandoval M., Marin-Castro H., Gonzalez-Compean J. Curve-Based Security Schemes for Automating the Encryption and Signing of Digital Documents in Organizational Environments. Program Comput Soft. 2021. V. 47. P. 849- 857.
  12. Jullien G., Miller W., Soltis J., Baraniecka A., Tseng B. Hardware realization of digital signal processing elements using the residue number system, in Proc. ICASSP 77. IEEE Int. Conf. Acoust., Speech, Signal Process., Hartford, CT, USA, 1977. P. 506- 510.
  13. Omondi A., Premkumar B. Residue Number Systems: Theory and Implementation, London: Imperial College Press, 2007.
  14. Cardarilli G.C., Nannarelli A., Re M. Residue number system for low- power DSP applications, in Proc. Asilomar Conf. Signals, Syst. Comput., 2007. P. 1412- 1416.
  15. Jullien G.A., Miller W.C. Application of the residue number system to computer processing of digital signals, in Proc. IEEE 4th Symp. on computer Arithmetic (ARITH), Santa Monica, CA, USA, 1978. P. 220-225.
  16. Chang C.H., Molahosseini A.S., Zarandi A.A.E., Tay T.F. Residue Number Systems: A New Paradigm to Datapath Optimization for Low-Power and High-Performance Digital Signal Processing Applications, in IEEE Circuits and Systems Magazine. 2015. V. 15. No 4. P. 26-44.
  17. Duong-Ngoc P., Kwon S., Yoo D., Lee H. Area-Efficient Number Theoretic Transform Architecture for Homomorphic Encryption, in IEEE Transactions on Circuits and Systems I: Regular Papers. 2023. V. 70. No 3. P. 1270-1283.
  18. Samimi N., Kamal M., Afzali-Kusha A., Pedram M. Res-DNN: A Residue Number System-Based DNN Accelerator Unit, in IEEE Transactions on Circuits and Systems I: Regular Papers. 2020. V. 67. No 2. P. 658-671.
  19. Chervyakov N.I., Lyakhov P.A., Nagornov N.N., Valueva M.V., Valuev G.V. Hardware implementation of a convolutional neural network using calculations in the residue number system, in Computer Optics. 2019. V. 43. No 5. P. 857-868.
  20. Kaplun D., Butusov D., Ostrovskii V., Veligosha A., Gulvanskii V. Optimization of the FIR Filter Structure in Finite Residue Field Algebra, in Electronics. 2018. V. 7. No 12. P. 372.
  21. Belghadr A., Jaberipur G. FIR Filter Realization via Deferred End-Around Carry Modular Addition, IEEE Transactions on Circuits and Systems I: Regular Papers. 2018. V. 65. No 9. P. 2878-2888.
  22. Lyakhov P., Valueva M., Valuev G., Nagornov N. High-Performance Digital Filtering on Truncated Multiply-Accumulate Units in the Residue Number System, in IEEE Access. 2020. V. 8. P. 209181-209190.
  23. Parhami B. Computer Arithmetic: Algorithms and Hardware Designs, London: Oxford Univ. Press, 2010.
  24. Kogge P.M., Stone H.S. A parallel algorithm for the efficient solution of a general class of recurrence equations, in IEEE Trans. Comput. 1973. V. C-22. No 8. P. 786-793.
  25. Molahosseini A.S., De Sousa L.S., Chang C.H. Embedded Systems Design with Special Arithmetic and Number Systems, New York: Springer, 2017.
  26. Mohan P.V.A., Phalguna P.S. Evaluation of Mixed-Radix Digit Computation Techniques for the Three Moduli RNS {2n-1, 2n, 2n+1 - 1} , IEEE Transactions on Circuits and Systems II: Express Briefs. 2021. V. 68. No 4. P. 1418-1422.
  27. Jaberipur G., Nadimi B. Balanced (3 + 2 log n) ΔG Adders for Moduli Set {2n+1, 2n + 2n-1 - 1}, 2n+1 - 1}, in IEEE Transactions on Circuits and Systems I: Regular Papers, 2020. P. 1- 10.
  28. Lyakhov P.A. Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system 2n - 1, 2n, 2n + 1 , in Journal of King Saud University - Computer and Information Sciences. 2023. V. 35. № 6. P. 101574.
  29. Lyakhov P. Improving the Parallelism and Balance of RNS with Low-Cost and 2k + 1 Modules, in Current Problems in Applied Mathematics and Computer Science and Systems (APAMCS 2022), 2023. V. 702.
  30. Srinivasa Reddy K., Sahoo S.K. An approach for fixed coefficient RNS-based FIR filter, in International Journal of Electronics. 2017. V. 104. № 8. P. 1358-1376.
  31. Belghadr A., Jaberipur G. Efficient variable-coefficient RNS-FIR filters with no restriction on the moduli set, in Signal, Image and Video Processing, 2022. P. 1443-1454.

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