Abstract
Finite Impulse Response (FIR) filters are widely used in biomedical, communication and audio signal processing applications due to their various advantages such as guaranteed stability and linear phase. The intelligent signal processing application with complex industrial systems can be implemented with the help of FIR filter design. In recent days, the design of the FIR filters is mainly controlled by the multiplication operations that lead to huge hardware utilization and delay. Therefore, the modified FIR filter is required to be developed with optimal multiplier and adder for improving the better performance in terms of hardware resource utilization and delay. In this paper, the 8-tap Fast FIR Algorithm (FFA) filter is proposed for decreasing hardware utilization. Here, the logical elements of the FFA filter are minimized using the Vedic multiplier (VM) and Carry Lookahead Adder (CLA). Additionally, the reduction in the logical elements leads to minimizing the delay which leads to increases in the operating frequency of the 8-tap FFA filter. Moreover, this proposed FFA-VM-CLA system is also analyzed in the Field Programmable Gate Array (FPGA) device of Spartan 6. The performance of the FFA-VM-CLA system is analyzed in terms of number of slice registers, flip flops, number of slices, Look Up Tables (LUTs), number of logical elements, slices, bonded Input/Output Block (IOB), delay, power and operating frequency. There are five different existing methods used to evaluate the FFA-VM-CLA system such as FIR-TDO, FIR-DNS, SLU-OBC-DA-FIR, FPPE and BP-FIR. The LUT of the FFA-VM-CLA system designed in the Virtex 5 is 174, it is less when compared to the FIR-TDO and SLU-OBC-DA-FIR.
Similar content being viewed by others
Data Availability
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
References
Soleimani, A. (2015). Combine particle swarm optimization algorithm and canonical sign digit to design finite impulse response filter. Soft Computing, 19(2), 407–419. https://doi.org/10.1007/s00500-014-1260-6
Agarwal, S., Rani, A., Singh, V., & Mittal, A. P. (2016). Performance Evaluation and Implementation of FPGA-Based SGSF in Smart Diagnostic Applications. Journal of Medical Systems, 40(3), 63. https://doi.org/10.1007/s10916-015-0404-2
Ding, F., Zhu, G., Li, Y., Zhang, X., Atrey, P.K., Lyu, S. (2021). Anti-Forensics for Face Swapping Videos via Adversarial Training. IEEE Transactions on Multimedia. https://doi.org/10.1109/TMM.2021.3098422
Yu, K., Tan, L., Lin, L., Cheng, X., Yi, Z., & Sato, T. (2021). Deep-Learning-Empowered Breast Cancer Auxiliary Diagnosis for 5GB Remote E-Health. IEEE Wireless Communications, 28(3), 54–61. https://doi.org/10.1109/MWC.001.2000374
Yu, K., Tan, L., Mumtaz, S., Al-Rubaye, S., Al-Dulaimi, A., Bashir, A. K., & Khan, F. A. (2021). Securing Critical Infrastructures: Deep Learning-based Threat Detection in the IIoT. IEEE Communications Magazine. https://doi.org/10.1109/MCOM.101.2001126
Yu, K., Arifuzzaman, M., Wen, Z., Zhang, D., & Sato, T. (2015). A Key Management Scheme for Secure Communications of Information Centric Advanced Metering Infrastructure in Smart Grid. IEEE Transactions on Instrumentation and Measurement, 64(8), 2072–2085.
Dwivedi, A.K., Ghosh, S., Londhe, N.D. (2016). Low power FIR filter design using modified multi-objective artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 55(C), 58–69. https://doi.org/10.1016/j.engappai.2016.06.006
Tan, L., Yu, K., Ming, F., Cheng, X., & Srivastava, G. (2021). Secure and Resilient Artificial Intelligence of Things: A HoneyNet Approach for Threat Detection and Situational Awareness. IEEE Consumer Electronics Magazine. https://doi.org/10.1109/MCE.2021.3081874
Ding, F., Yu, K., Gu, Z., Li, X., Shi, Y. (2021). Perceptual Enhancement for Autonomous Vehicles: Restoring Visually Degraded Images for Context Prediction via Adversarial Training. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3120075
Yu, K. et al. (2021). A Blockchain-based Shamir’s Threshold Cryptography Scheme for Data Protection in Industrial Internet of Things Settings. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3125190
Ding, F., Zhu, G., Alazab, M., Li, X., Yu, K. (2020). Deep-Learning-Empowered Digital Forensics for Edge Consumer Electronics in 5G HetNets. IEEE Consumer Electronics Magazine. https://doi.org/10.1109/MCE.2020.3047606
Sundar, P. V. P., Ranjith, D., Karthikeyan, T., Kumar, V. V., & Jeyakumar, B. (2020). Low power area efficient adaptive FIR filter for hearing aids using distributed arithmetic architecture. International Journal of Speech Technology, 23(2), 287–296. https://doi.org/10.1007/s10772-020-09686-y
Dwivedi, A. K., Ghosh, S., & Londhe, N. D. (2016). Modified artificial bee colony optimisation based FIR filter design with experimental validation using field-programmable gate array. IET Signal Processing, 10(8), 955–964. https://doi.org/10.1049/iet-spr.2015.0214
Singh, G., & Prakash, N. R. (2017). FPGA implementation of higher order FIR filter. International Journal of Electrical and Computer Engineering, 7(4), 1874–1881. https://doi.org/10.11591/ijece.v7i4.pp1874-1881
Vandenbussche, J.-J., Peuteman, J., & Lee, P. (2015). Multiplicative finite impulse response filters: Implementations and applications using field programmable gate arrays. IET Signal Processing, 9(5), 449–456. https://doi.org/10.1049/iet-spr.2014.0143
Tan, L., Yu, K., Lin, L., Srivastava, G., Lin, J. C., Wei, W. (2021). Speech Emotion Recognition Enhanced Traffic Efficiency Solution for Autonomous Vehicles in a 5G-Enabled Space-Air-Ground Integrated Intelligent Transportation System. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3119921
Sun, Y., Liu, J., Yu, K., Alazab, M., Lin, K. (2021). PMRSS: Privacy-preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3070544
Guo, T., Yu, K., Aloqaily, M., & Wan, S. (2021). Constructing a Prior-dependent Graph for Data Clustering and Dimension Reducton in the Edge of AIoT. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2021.09.044
Guo, Z., Yu, K., Jolfaei, A., Bashir, A. K., Almagrabi, A. O., Kumar, N. (2021). A Fuzzy Detection System for Rumors through Explainable Adaptive Learning. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2021.3052109
Pandey, B., Pandey, N., Kaur, A., Hussain, D. M. A., Das, B., & Tomar, G. S. (2019). Scaling of Output Load in Energy Efficient FIR Filter for Green Communication on Ultra-Scale FPGA. Wireless Personal Communications, 106(4), 1813–1826. https://doi.org/10.1007/s11277-018-5717-2
Feng, C., et al. (2020). Attribute-Based Encryption with Parallel Outsourced Decryption for Edge Intelligent IoV. IEEE Transactions on Vehicular Technology, 69(11), 13784–13795. https://doi.org/10.1109/TVT.2020.3027568
Tan, L., Yu, K., Shi, N., Yang, C., Wei, W., Lu, H. (2021). Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2021.3101842
Li, H., Yu, K., Liu, B., Feng, C., Qin, Z., Srivastava, G. (2021). An Efficient Ciphertext-Policy Weighted Attribute-Based Encryption for the Internet of Health Things. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2021.3075995
Yu, K., Guo, Z., Shen, Y., Wang, W., Lin, J. C., & Sato, T. (2021). Secure Artificial Intelligence of Things for Implicit Group Recommendations. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3079574
Gong, Y., Zhang, L., Liu, R., Yu, K., & Srivastava, G. (2021). Nonlinear MIMO for Industrial Internet of Things in Cyber-Physical Systems. IEEE Transactions on Industrial Informatics, 17(8), 5533–5541. https://doi.org/10.1109/TII.2020.3024631
Pandey, B., Das, B., Kaur, A., Kumar, T., Khan, A. M., Hussain, D. M. A., & Tomar, G. S. (2017). Performance Evaluation of FIR Filter After Implementation on Different FPGA and SOC and Its Utilization in Communication and Network. Wireless Personal Communications, 95(2), 375–389. https://doi.org/10.1007/s11277-016-3898-0
Egila, M. G., El-Moursy, M. A., El-Hennawy, A. E., El-Simary, H. A., & Zaki, A. (2016). FPGA-based electrocardiography (ECG) signal analysis system using least-square linear phase finite impulse response (FIR) filter. Journal of Electrical Systems and Information Technology, 3(3), 513–526. https://doi.org/10.1016/j.jesit.2015.07.001
Chitra, E., Vigneswaran, T., & Malarvizhi, S. (2018). Analysis and Implementation of High Performance Reconfigurable Finite Impulse Response Filter Using Distributed Arithmetic. Wireless Personal Communications, 102(4), 3413–3425. https://doi.org/10.1007/s11277-018-5375-4
Saranya, R., Pradeep, C., & Radhakrishnan, R. (2017). Design and implementation of a reconfigurable finite impulse response filter for adaptive systems. International Journal of Computational Systems Engineering, 3(1–2), 82–90. https://doi.org/10.1504/IJCSYSE.2017.083158
Srinivas, N., Pradhan, G., & Kumar, P. K. (2020). A Classification-Based Non-local Means Adaptive Filtering for Speech Enhancement and Its FPGA Prototype. Circuits, Systems, and Signal Processing, 39(5), 2489–2506. https://doi.org/10.1007/s00034-019-01267-y
Feng, C., Liu, B., Guo, Z., Yu, K., Qin, Z., & Choo, K.-K.R. (2021). Blockchain-based Cross-domain Authentication for Intelligent 5G-enabled Internet of Drones. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3113321
Feng, C., Liu, B., Yu, K., Goudos, S. K., Wan, S. (2021). Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3116132
Guo, Z., Yu, K., Li, Y., Srivastava, G., & Lin, J. C.-W. (2021). Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2021.3049262
Dwivedi, A. K., Ghosh, S., & Londhe, N. D. (2017). Low-Power FIR Filter Design Using Hybrid Artificial Bee Colony Algorithm with Experimental Validation Over FPGA. Circuits, Systems, and Signal Processing, 36(1), 156–180. https://doi.org/10.1007/s00034-016-0297-4
Jyothi, G. N., Sanapala, K., & Vijayalakshmi, A. (2020). ASIC implementation of distributed arithmetic based FIR filter using RNS for high speed DSP systems. International Journal of Speech Technology, 23, 259–264. https://doi.org/10.1007/s10772-020-09683-1
Khurshid, B., & Mir, R. N. (2017). An Efficient FIR Filter Structure Based on Technology-Optimized Multiply-Adder Unit Targeting LUT-Based FPGAs. Circuits, Systems, and Signal Processing, 36(2), 600–639. https://doi.org/10.1007/s00034-016-0312-9
Diaz, C., Sanchez, G., Avalos, J.G., Sanchez, G., Sanchez, J.C., & Perez, H. (2017). Spike-based compact digital neuromorphic architecture for efficient implementation of high order FIR filters. Neurocomputing, 251(C), 90–98. https://doi.org/10.1016/j.neucom.2017.04.012
Sanchez, G., Diaz, C., Avalos, J. G., Garcia, L., Vazquez, A., Toscano, K., Sanchez, J. C., & Perez, H. (2019). A Highly Scalable Parallel Spike-Based Digital Neuromorphic Architecture for High-Order FIR Filters Using LMS Adaptive Algorithm. Neurocomputing, 330, 425–436. https://doi.org/10.1016/j.neucom.2018.10.029
Prasad, J., Geetha, D. M., & Srinivasan, K. (2019). Experimental setup of stretchable arid dry pad sensors for the signal acquisition FIR filter design using Vedic approach. Measurement, 141, 209–216. https://doi.org/10.1016/j.measurement.2019.02.083
Roy, S., & Chandra, A. (2019). On the order minimization of interpolated bandpass method based narrow transition band FIR filter design. IEEE Transactions on Circuits and Systems I: Regular Papers, 66(11), 4287–4295. https://doi.org/10.1109/TCSI.2019.2928052
Kalaiyarasi, D., & Reddy, T. K. (2019). Design and implementation of Least Mean Square adaptive FIR filter using offset binary coding based Distributed Arithmetic. Microprocessors and microsystems, 71, 102884. https://doi.org/10.1016/j.micpro.2019.102884
Chowdari, P., & Seventline, J. B. (2020). VLSI implementation of distributed arithmetic based block adaptive finite impulse response filter. Materials Today: Proceedings, 33(P7), 3757–3762. https://doi.org/10.1016/j.matpr.2020.06.206
NagaJyothi, G., & Sridevi, S. (2020). High speed low area OBC DA based decimation filter for hearing aids application. International Journal of Speech Technology, 23(1), 111–121. https://doi.org/10.1007/s10772-019-09660-3
John, T.M., & Chacko, S. (2021). FPGA‐based implementation of floating point processing element for the design of efficient FIR filters. IET Computers & Digital Techniques.
Sikka, P., Asati, A. R., & Shekhar, C. (2021). Area, Speed and Power Optimized Implementation of a Band-Pass FIR Filter Using High-Level Synthesis. Wireless Personal Communications, 1-10.
Funding
This research received no external funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pondreti, P., Babulu, K. Low Area High-speed Hardware Implementation of Fast FIR Algorithm for Intelligent Signal Processing application in Complex Industrial Systems. J Sign Process Syst 95, 225–240 (2023). https://doi.org/10.1007/s11265-022-01762-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-022-01762-7