Statistical Modelling And Electronic Design Optimization In Bioinformatics Devices: A Multidisciplinary Study On Data Processing And Power Efficiency

Chandan Kumar Choubey; M. Dinesh; Dr. Rashid Ali; Dr. Deepti Seth; Pallab Paul; Dr. R. Kalidasan

doi:10.29284/mh0pgk90

Authors

Chandan Kumar Choubey Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India
M. Dinesh Assistant Professor, Electrical and Electronics Engineering, Adhi College of Engineering and Technology, Kanchipuram
Dr. Rashid Ali Professor, Department of Applied Sciences and Humanities, Krishna Institute of Engineering and Technology (KIET), Delhi-NCR, Ghaziabad, India Orcid ID: https://orcid.org/0000-0001-6674-393X
Dr. Deepti Seth Associate Professor, Department of Applied Sciences and Humanities, Krishna Institute of Engineering & Technology (KIET), Ghaziabad, Delhi-NCR, Uttar Pradesh, India, Orcid ID: https://orcid.org/0000-0001-8943-0194
Pallab Paul Assistant Professor, Computer Science & Engineering (CS&DS), Brainware University, Barasat, Orcid ID: 0009000155326396
Dr. R. Kalidasan School of Mechanical Engineering, Lovely Professional University, Orcid ID: 0000-0001-5366-9822

DOI:

https://doi.org/10.29284/mh0pgk90

Keywords:

statistical modelling, electronic design Optimization, energy efficiency, bioinformatics devices, theoretical framework.

Abstract

Statistical modelling plays a pivotal role in extracting insights from complex biological data, while electronic design governs the physical efficiency of the systems processing that data. Despite advancements in both domains, bioinformatics devices often suffer from fragmented design approaches that separate algorithmic precision from power efficiency. To address this, disconnect, a unified theoretical framework is developed that jointly optimizes data fidelity and energy consumption. The proposed model introduces a composite objective function that combines statistical loss with hardware-level energy cost, subject to practical constraints such as thermal limits and bandwidth. Mathematical proofs confirm the existence, uniqueness, and stability of optimal solutions, while symbolic analysis reveals a nonlinear trade-off between computational accuracy and power savings. The framework supports predictive reasoning and offers analytical tools for balancing performance under resource constraints. Unlike empirical tuning methods, this approach ensures theoretical consistency and broad applicability across system architectures. With potential relevance to embedded genomic devices and real-time biomedical applications, the model lays the groundwork for co-optimized designs that are both mathematically rigorous and energy-aware. Future work may explore integration with empirical datasets, circuit simulators, and emerging hardware technologies to validate and expand its applicability.

References

1. B. Berger and Y. W. Yu, "Navigating bottlenecks and trade-offs in genomic data analysis," Nature Reviews Genetics, vol. 24, no. 4, pp. 235–250, 2023.

2. J. Grealey et al., "The carbon footprint of bioinformatics," Molecular Biology and Evolution, vol. 39, no. 3, article msac034, 2022.

3. S. Rawat, "The need for speed and energy efficiency in genome analysis," GEN Biotechnology, vol. 2, no. 3, pp. 157–161, 2023.

4. M. Alser et al., "From molecules to genomic variations: accelerating genome analysis via intelligent algorithms and architectures," Computational and Structural Biotechnology Journal, vol. 20, pp. 4579–4599, 2022.

5. T. Robinson, J. Harkin, and P. Shukla, "Hardware acceleration of genomics data analysis: challenges and opportunities," Bioinformatics, vol. 37, no. 13, pp. 1785–1795, 2021.

6. V. S. Raj, A. Priyadarshini, M. K. Yadav, R. P. Pandey, A. Gupta, and A. Vibhuti, "Artificial intelligence in bioinformatics," Biomedical Data Mining for Information Retrieval: Methodologies, Techniques and Applications, pp. 21–51, 2021.

7. J. L. Hennessy and D. A. Patterson, "A new golden age for computer architecture," Communications of the ACM, vol. 62, no. 2, pp. 48–60, 2019.

8. A. Mirhoseini et al., "A graph placement methodology for fast chip design," Nature, vol. 594, no. 7862, pp. 207–212, 2021.

9. M. Golzan et al., "Analog and mixed-signal IC modelling and optimization: an artificial intelligence perspective," ACM Transactions on Design Automation of Electronic Systems, vol. 30, no. 5, article 88, 2025.

10. M. Masadeh, O. Hasan, and S. Tahar, "Machine-learning-based self-tunable design of approximate computing," IEEE Transactions on Very Large Scale Integration Systems, vol. 29, no. 4, pp. 800–813, 2021.

11. W. Liu, F. Lombardi, and M. Schulte, "A retrospective and prospective view of approximate computing," Proceedings of the IEEE, vol. 108, no. 3, pp. 394–399, 2020.

12. K. G. Liakos and F. Plessas, "Analog design and machine learning: a review," Electronics, vol. 14, no. 17, p. 3541, 2025.

13. Y. Turakhia, G. Bejerano, and W. J. Dally, "Darwin: a genomics coprocessor," IEEE Micro, vol. 39, no. 3, pp. 29–37, 2019.

14. Z. Liang et al., "An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile HMMs," BMC Bioinformatics, vol. 25, article 247, 2024.

15. Y. Feng et al., "FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis," BMC Bioinformatics, vol. 26, article 83, 2025.

16. J. Hennessy and D. Patterson, “A new golden age for computer architecture: domain-specific hardware/software co-design, enhanced," in Proceedings of the ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), June 2018.

17. J. Shalf, "The future of computing beyond Moore's Law," Philosophical Transactions of the Royal Society A, vol. 378, no. 2166, p. 20190061, 2020.

18. V. Leon et al., "Approximate computing survey, Part I: terminology and software & hardware approximation techniques," ACM Computing Surveys, vol. 57, no. 2, article 37, 2025.

19. A. Goldie et al., "Addendum: A graph placement methodology for fast chip design," Nature, vol. 634, no. 8034, pp. E10–E11, 2024.

20. S. Samarasinghe, P. Premathilaka, W. Herath, H. Gamaarachchi, and R. Ragel, "Energy efficient adaptive banded event alignment using OpenCL on FPGAs," in Proceedings of the 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), Aug. 2021, pp. 369–374, IEEE.

21. A. Alonso-Marín, I. Fernandez, Q. Aguado-Puig, J. Gómez-Luna, S. Marco-Sola, O. Mutlu, and M. Moreto, "BIMSA: accelerating long sequence alignment using processing-in-memory," Bioinformatics, vol. 40, no. 11, p. btae631, 2024.

22. M. Alser et al., "Accelerating genome analysis: a primer on an ongoing journey," IEEE Micro, vol. 40, no. 5, pp. 65–75, 2020.

23. B. Tam et al., "MRI-based brain tumor segmentation using FPGA-accelerated hardware," BMC Bioinformatics, vol. 22, article 421, 2021.

24. T. Anderson and T. J. Wheeler, "An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models," BMC Bioinformatics, vol. 25, no. 1, p. 247, 2024.

25. A. C. Jacob, J. M. Lancaster, J. D. Buhler, and R. D. Chamberlain, “Preliminary results in accelerating profile HMM search on FPGAs," in Proceedings of the 2007 IEEE International Parallel and Distributed Processing Symposium, Mar. 2007, pp. 1–8, IEEE.