  The field of artificial intelligence has significantly advanced over the past
decades, inspired by discoveries from the fields of biology and neuroscience.
The idea of this work is inspired by the process of self-organization of
cortical areas in the human brain from both afferent and lateral/internal
connections. In this work, we develop an original brain-inspired neural model
associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant
SOM (ReSOM) model. The framework is applied to multimodal classification
problems. Compared to existing methods based on unsupervised learning with
post-labeling, the model enhances the state-of-the-art results. This work also
demonstrates the distributed and scalable nature of the model through both
simulation results and hardware execution on a dedicated FPGA-based platform
named SCALP (Self-configurable 3D Cellular Adaptive Platform). SCALP boards can
be interconnected in a modular way to support the structure of the neural
model. Such a unified software and hardware approach enables the processing to
be scaled and allows information from several modalities to be merged
dynamically. The deployment on hardware boards provides performance results of
parallel execution on several devices, with the communication between each
board through dedicated serial links. The proposed unified architecture,
composed of the ReSOM model and the SCALP hardware platform, demonstrates a
significant increase in accuracy thanks to multimodal association, and a good
trade-off between latency and power consumption compared to a centralized GPU
implementation.
