An energy efficient approach for C4.5 algorithm using OpenCL design flow

C4.5 is an important data mining algorithm and has been widely applied in applications including face detection, character recognition and predictive analysis. Although C4.5 is highly accurate, its training process is time-consuming because the traditional algorithm has limited parallelism and frequent data hazards. In order to address these issues, we propose an energy efficient approach for C4.5 training with parallel search, low-latency memory accesses and a folded programming structure to achieve higher acceleration and energy efficiency. This proposed approach greatly improves the C4.5 training process by using a CPU-FPGA heterogeneous platform and OpenCL design flow. Three UCI data sets including spambase, Magic04, and MiniBooNE, are used to evaluate the performance of our method. Experimental results show that our approach has achieved 58X, 63X, 380X higher energy efficiency than the basic serial C4.5 algorithm (serial SPRINT) running on an Intel i7- 3770K CPU and 10X, 1.6X, 12.7X higher energy efficiency than the existing parallel C4.5 algorithm (parallel SPRINT) running on the same CPU-FPGA heterogeneous platform. We also deliver 3X, 5X, 3X higher energy efficiency than the existing CUDA based parallel C4.5 (CUDT) in CPU-GPU heterogeneous platform when using these three datasets.