The field of computer systems is witnessing significant advancements in FPGA-based acceleration and real-time systems. Researchers are exploring innovative ways to leverage FPGAs to improve performance, reduce energy consumption, and enhance reliability in various applications, including genomics, computer vision, and cryptography. Notably, FPGAs are being used to accelerate pairwise sequence alignment, machine learning models, and cryptographic operations, achieving substantial speedups and energy efficiency gains compared to traditional CPU and GPU implementations. Additionally, real-time systems are being optimized to achieve deterministic sub-microsecond response times, enabling more reliable and efficient operation in applications such as robotics and autonomous vehicles. Overall, these developments are pushing the boundaries of what is possible in computer systems, enabling new applications and use cases that were previously unimaginable.

Noteworthy papers include: GeneTEK, which presents a scalable and flexible FPGA-based accelerator for genome sequence matching, achieving significant performance and energy efficiency gains. FASE, which introduces an FPGA-Assisted Syscall Emulation framework for rapid end-to-end processor performance validation, demonstrating high accuracy and efficiency in validating complex multi-thread benchmarks.