  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.
