Support Tool for the Combined Software/Hardware Design of On-Chip ELM Training for SLFF Neural Networks

Abstract

Typically, hardware implemented neural networks are trained before implementation. Extreme learning machine (ELM) is a noniterative training method for single-layer feed-forward (SLFF) neural networks well suited for hardware implementation. It provides fixed-time learning and simplifies retraining of a neural network once implemented, which is very important in applications demanding on-chip training. This study proposes the data flow of a software support tool in the design process of a hardware implementation of on-chip ELM learning for SLFF neural networks. The software tool allows the user to obtain the optimal definition of functional and hardware parameters for any application, and enables the user to interact throughout the design process. Combining in a transparent way for the user, simulation and Xilinx synthesis tools, the tool recommends the optimal configuration, generating, finally, a synthesizable IP-core. As application, the field-programmable gate array implementation for real-time detection of brain areas in electrode positioning during a deep brain stimulation surgery is described. The generated IP-core can execute a peak of 95 ELM trainings per second on a low-cost Spartan 6 device, making possible its real-time use in this application.