Year: 2023
Type: preprint
Abstract: Neural networks for industrial applications generally have additional constraints such as response speed, memory size and power usage. Randomized learners can address some of these issues. However, hardware solutions can provide better resource reduction whilst maintaining the model's performance. Stochastic configuration networks (SCNs) are a prime choice in industrial applications due to their merits and feasibility for data modelling. Stochastic Configuration Machines (SCMs) extend this to focus on reducing the memory constraints by limiting the randomized weights to a binary value with a scalar for each node and using a mechanism model to improve the learning performance and result interpretability. This paper aims to implement SCM models on a field programmable gate array (FPGA) and introduce binary-coded inputs to the algorithm. Results are reported for two benchmark and two industrial datasets, including SCM with single-layer and deep architectures.
Source: arXiv (Cornell University)
Authors: Felicetti, Matthew J., Wang, Dianhui
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4388093273