Random Forest Architectures on FPGA for Multiple Applications

A random forest is a widely used machine learning classifier. An FPGA is a good platform for performance acceleration of random forests due to their inherent concurrent memory accesses and computational parallelism. Amortizing the cost of an FPGA implementation among different applications is desirable, but the context switch time between applications significantly influences the forest architecture. Several architectures for random forests implemented within an FPGA are described, and the area-reconfiguration tradeoffs that determine the suitability of each for multiple applications are characterized. We show that each architecture can maximize the area utilization efficiency of an FPGA given a constraint on the context switch time.