High-throughput implementation of a million-point sparse Fourier Transform

Abhinav Agarwal, Haitham Hassanieh, Omid Abari, Ezz Hamed, Dina Katabi, Arvind
2014 2014 24th International Conference on Field Programmable Logic and Applications (FPL)  
The emergence of data-intensive problems in areas like computational biology, astronomy, medical imaging, etc. has emphasized the need for fast and efficient very large Fourier Transforms. Recent work has shown that we can compute million-point transforms efficiently provided the data is sparse in the frequency domain. Processing input samples at rates approaching 1 GHz would allow real-time processing in several such applications. In this paper, we present a high-throughput FPGA implementation
more » ... FPGA implementation that performs a million-point sparse Fourier Transform on frequency-sparse input data, generating the largest 500 frequency component locations and values every 1.16 milliseconds. This design can process streamed input data at 0.86 Giga samples per second, and does not make any assumptions of the distribution of the frequency components beyond sparsity.
doi:10.1109/fpl.2014.6927450 dblp:conf/fpl/AgarwalHAHKA14 fatcat:algozemhmrhh3n5prirgjirpfy