Accelerating Extreme Learning Machine on FPGA by Hardware Implementation of Given Rotation-QRD

Abstract

Currently, Extreme Learning Machine (ELM) is one of the research trends in the machine learning field due to its remarkable performances in terms of complexity and computational speed. However, the big data era and the limitations of general-purpose processor cause the increasing of interest in hardware implementation of ELM in order to reduce the computational time. Hence, this work presents the hardware-software co-design of ELM to improve the overall performances. In the co-design paradigm, one of the important components of ELM, namely Given Rotation-QRD (GR-QRD) is developed as a hardware core. Field Programmable Gate Array (FPGA) is chosen as the platform for ELM implementation due to its reconfigurable capability and high parallelism. Moreover, the learning accuracy and computational time would be used to evaluate the performances of the proposed ELM design. Our experiment has shown that GR-QRD accelerator helps to reduce the computational time of ELM training by 41.75% while maintaining the same training accuracy in comparison to pure software of ELM.