Sizing of Processing Arrays for FPGA-Based Computation

Abstract

Computing applications in FPGAs are commonly built from repetitive structures of computing and/or memory elements. In many cases, application performance depends on the degree of parallelism - ideally, the most that will fit into the fabric of the FPGA being used. Several factors complicate determination of the largest structure that will fit the FPGA: arrays that grow nonlinearly and in uneven step sizes, coupled structures that grow in different polynomial order, multiple design parameters controlling different aspects of the computing structure, and interlocked usage of different hardware resources. Combined with resource usage that depends on application-specific data elements and arithmetic details, these factors defeat any simple approach for scaling the computing structures up to the FPGA's capacity. We present a formal analysis of maximizing FPGA utilization, with adaptations that simplify the optimization problem. We also report on design tools containing extensions that support automated sizing of FPGA-based computation arrays