Implementation of FPGA-Based Digital Signal Processing Architecture for Real-Time Applications

Abstract

Field-programmable gate arrays (FPGAs) are widely used for computationally intensive digital signal processing (DSP) because their fine-grained parallelism, dedicated multiplier-accumulator resources, on-chip memories, and deterministic timing can be matched to streaming real-time workloads. This paper presents a methodology for implementing a reusable FPGA-based DSP architecture intended for filtering, spectral analysis, and related real-time applications. The proposed architecture combines fixed-point arithmetic, deep pipelining, parallel multiply-accumulate units, FIFO-based streaming interfaces, block RAM for coefficients and state, and a finite-state control layer. A stepwise implementation flow is defined from algorithm specification and word-length selection through RTL/high-level synthesis, simulation, synthesis, timing closure, and hardware validation. The evaluation framework uses a 64-tap finite impulse response (FIR) kernel as a representative workload and derives analytical throughput and latency at a 125 MHz target clock. A fully parallel pipelined realization can accept one new sample per clock after pipeline filling, corresponding to a theoretical 125 MS/s input rate, whereas a single-MAC sequential realization requires 64 cycles per sample and is limited to about 1.95 MS/s at the same clock. The methodology therefore demonstrates how FPGA spatial parallelism can be traded against DSP-block utilization to meet deterministic real-time constraints. The paper also discusses verification, fixed-point accuracy, resource-performance trade-offs, and limitations of synthesis-oriented results prior to board-level measurement.