FPGA Implementation of Non-Uniform DFT for Accelerating Wireless Channel Simulations (Abstract Only)
Title | FPGA Implementation of Non-Uniform DFT for Accelerating Wireless Channel Simulations (Abstract Only) |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Siripurapu, Srinivas, Gayasen, Aman, Gopalakrishnan, Padmini, Chandrachoodan, Nitin |
Conference Name | Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4354-1 |
Keywords | Acceleration, analogical transfer, analogies, FPGAs, high performance computation, high-level synthesis, Human Behavior, human factors, NUDFT, pubcrawl, Transference, wireless channel simulation |
Abstract | FPGAs have been used as accelerators in a wide variety of domains such as learning, search, genomics, signal processing, compression, analytics and so on. In recent years, the availability of tools and flows such as high-level synthesis has made it even easier to accelerate a variety of high-performance computing applications onto FPGAs. In this paper we propose a systematic methodology for optimizing the performance of an accelerated block using the notion of compute intensity to guide optimizations in high-level synthesis. We demonstrate the effectiveness of our methodology on an FPGA implementation of a non-uniform discrete Fourier transform (NUDFT), used to convert a wireless channel model from the time-domain to the frequency domain. The acceleration of this particular computation can be used to improve the performance and capacity of wireless channel simulation, which has wide applications in the system level design and performance evaluation of wireless networks. Our results show that our FPGA implementation outperforms the same code offloaded onto GPUs and CPUs by 1.6x and 10x respectively, in performance as measured by the throughput of the accelerated block. The gains in performance per watt versus GPUs and CPUs are 15.6x and 41.5x respectively. |
URL | https://dl.acm.org/citation.cfm?doid=3020078.3021800 |
DOI | 10.1145/3020078.3021800 |
Citation Key | siripurapu_fpga_2017 |