Fixed-Point Evaluation of Extreme Learning Machine for Classification

Abstract

With growth of data sets, the efficiency of Extreme Learning Machine (ELM) model combined with accustomed hardware implementation such as Field-programmable gate array (FPGA) became attractive for many real-time learning tasks. In order to reduce resource occupation in eventual trained model on FPGA, it is more efficient to store fixed-point data rather than double-floating data in the on-chip RAMs. This paper conducts the fixed-point evaluation of ELM for classification. We converted the ELM algorithm into a fixed-point version by changing the operation type, approximating the complex function and blocking the large-scale matrixes, according to the architecture ELM would be implemented on FPGA. The performance of classification with single bit-width and mixed bit-width were evaluated respectively. Experimental results show that the fixed-point representation used on ELM does work for some application, while the performance could be better if we adopt mixed bit-width.