An Efficient FPGA-Based Architecture for High-Speed Digital Signal Processing in Advanced VLSI Systems

Abstract

The rising pressure on real time based data processing in the current communication, multimedia and embedded applications has acted as a major boost to the need of high performing Digital Signal Processing (DSP) systems. Traditional processor-based implementations could be insufficient to achieve very strict timing and throughput specifications since they have low parallelism and an increased computation latency. Fields-Programmable Gate Arrays (FPGAs) in this regard have become an attractive platform, with a combination of reconfigurability, parallel nature and efficient hardware use to accelerate DSP work. The paper suggests an effective FPGA architecture that is used in the high-speed DSP applications of advanced systems-on-a-chip VLSI. The various optimization techniques (paralleled processing, deep pipelining, optimization arithmetic units, including pipelined trees, high-speed multipliers, etc.) are considered in the proposed design, to reduce the critical path delay and improve the computational performance. Moreover, the architecture has made good use of the FPGA resources including DSP slices, look up tables (LUTs) and registers to attain a balanced trade-off between performance, area and power consumption. The system will be measured based on conventional performance measurements, such as throughput, latency, resource usage, and power usage. The experimental outcomes prove that the given architecture is much more efficient than the traditional FPGA-based DSP implementations with higher operating frequency, less processing delay, and energy efficiency. These are the improvements that render the proposed system very suitable to real-time and highly throughput application like wireless communication, video processing, and software-defined radio. Generally, the paper has identified the usefulness of integrating architectural optimization methods with FPGA capabilities to address the needs of the scaling computational demands of the new DSPs.