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The rapid growth of Internet-of-things (IoT) and artificial intelligenceapplications have called forth a new computing paradigm--edge computing. Inthis paper, we study the suitability of deploying FPGAs for edge computing fromthe perspectives of throughput sensitivity to workload size, architecturaladaptiveness to algorithm characteristics, and energy efficiency. This goal isaccomplished by conducting comparison experiments on an Intel Arria 10 GX1150FPGA and an Nvidia Tesla K40m GPU. The experiment results imply that the keyadvantages of adopting FPGAs for edge computing over GPUs are three-fold: 1)FPGAs can provide a consistent throughput invariant to the size of applicationworkload, which is critical to aggregating individual service requests fromvarious IoT sensors; (2) FPGAs offer both spatial and temporal parallelism at afine granularity and a massive scale, which guarantees a consistently highperformance for accelerating both high-concurrency and high-dependencyalgorithms; and (3) FPGAs feature 3-4 times lower power consumption and up to30.7 times better energy efficiency, offering better thermal stability andlower energy cost per functionality.

Are FPGAs Suitable for Edge Computing?