FPGA-Based Real-Time Stream Processing and Parallel Computing Optimization Techniques

Real-time stream processing in big-data analytics and modern communications requires high bandwidth and low end-to-end latency, while general-purpose processors face scaling limits due to memory traffic, synchronization overhead, and diminishing parallel efficiency. This paper presents an FPGA-based co-optimization framework for real-time dataflow workloads. We combine (i) pipeline restructuring that separates memory access from computation and enables task-level dataflow, (ii) dynamic resource scheduling that mitigates congestion under multi-tenant workloads, and (iii) data sharding parallelism with on-chip buffer reuse to increase effective bandwidth. Across representative streaming kernels and multi-task mixes, the proposed approach improves sustained throughput by 62% and reduces end-to-end latency by 45%. Compared with a GPU baseline at matched output quality, energy consumption is reduced by 38%, while maintaining stable tail latency under bursty inputs.