FPGA Based Acceleration of Biological Sequence Alignment Algorithms Based on a Dynamic Programming Approach: A Survey

The rapid growth of biological sequence data necessitates efficient computational methods for sequence alignment, a fundamental task in bioinformatics. In this study, we provide a thorough overview of the literature on the topic of employing Dynamic Programming (DP) to speed up methods for biological sequence alignment using Field-Programmable Gate Arrays (FPGAs). Needleman-Wunsch and Smith-Waterman are two examples of DP algorithms that ensure optimum alignment; nonetheless, they are computationally demanding. FPGAs offer a promising platform for accelerating these algorithms by exploiting parallelism and hardware customization. This survey reviews existing research and methodologies employed to implement sequence alignment algorithms on FPGAs, comparing their performance, scalability, and energy efficiency against traditional CPU-based approaches. We also discuss the challenges and opportunities associated with FPGA-based acceleration, including data dependencies, reconfigurability, and optimization techniques. Hardware-software co-design and advancements in FPGA architectures can further enhance performance and scalability in biological sequence alignment tasks. Alignment accuracy may be improved by using Machine Learning (ML) models like Convolutional Neural Networks (CNNs) to extract features from raw sequence data. In order to facilitate future developments in this dynamic area, the study seeks to enlighten scholars and practitioners on the cutting-edge acceleration methods for biological sequence alignment that are based on FPGAs.