FPGA Based High-Throughput Real-Time Feature Extraction for Modulation Classification

摘要

The spectral correlation density (SCD) function is a feature extraction method used in signal classification systems. Due to its computational complexity, SCD has not been a desirable method for systems under power and real-time constraints. In this study, we present results for a hardware implementation of key kernels of the SCD function on a Field Programmable Gate Array (FPGA). By analyzing profiling results for a state of the art GPU implementation, we developed a preliminary architecture that is able to accelerate the most computationally demanding aspects of the SCD algorithm. We find that this FPGA architecture is able to achieve a 2.03X speedup relative to state of the art GPU-based SCD implementations by coupling SCD's large-scale data-parallel nature with an architecture well suited for fine-grained control flow and data access patterns.