Accelerating 3D convolution using streaming architectures on FPGAs

Accelerating 3D convolution using streaming architectures on FPGAs

Haohuan Fu, Robert G. Clapp, Oskar Mencer, and Oliver Pell

Abstract:

We investigate FPGA architectures for accelerating applications whose dominant cost is 3D convolution, such as modeling and Reverse Time Migration (RTM). We explore different design options, such as using different stencils, fitting multiple stencil operators into the FPGA, processing multiple time steps in one pass, and customizing the computation precisions. The exploration reveals constraints and tradeoffs between different design parameters and metrics. The experiment results show that the FPGA streaming architecture provides great potential for accelerating 3D convolution, and can achieve up to two orders of magnitude speedup.