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In recent years, Network-on-Chip (NoC) has emerged as a promising solution for addressing a critical performance bottleneck encountered in designing large-scale multi-core systems, i.e., data communication. With advancements in chip manufacturing technologies and the increasing complexity of system designs, the task of designing the communication sub- systems has become increasingly challenging. The emergence of hardware accelerators, such as GPUs, FPGAs and ASICs, together with heterogeneous system integration of the CPUs and the accelerators creates new challenges in NoC design. Conventional NoC architectures developed for CPU-based multi- core systems are not able to satisfy the traffic demands of heterogeneous systems. In recent years, numerous research efforts have been dedicated to exploring the various aspects of NoC design in hardware accelerators and heterogeneous systems. However, there is a need for a comprehensive understanding of the current state-of-the-art research in this emerging research area. This paper aims to provide a summary of research work conducted in heterogeneous NoC design. Through this survey, we aim to present a comprehensive overview of the current related research, highlighting key findings, challenges, and future directions in this field.
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This content will become publicly available on December 16, 2025
Survey of Hardware Acceleration of Genomic Analysis
As the Next-Generation Sequencing (NGS) techniques need to process enormous amounts of data, cost-efficientfand high-throughput computational analysis is essential in genomicsfstudy. Conventional computing platforms face great challenges to meet these demands due to their limited processing speed and scalability. Hardware accelerators, such as Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), offer transformative solutions to these computational challenges. This paper provides a state-of-the-art review of the roles of hardware accelerators in genomic analysis.We performed a comprehensive and in-depth analysis of cutting-edge genomics hardware accelerators, such as GPUs, FPGAs, and ASICs, in the context of the specific algorithms they aim to enhance. Besides reviewing opportunities in hardware genome acceleration, we also provide insights into the challenges regarding processing speed, cost efficiency, and scalability.
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- PAR ID:
- 10552567
- Publisher / Repository:
- IEEE
- Date Published:
- Subject(s) / Keyword(s):
- genome analysis genome sequencing hardware accelerator GPU FPGA ASIC accelerated computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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