ModNEF : An Open Source Modular Neuromorphic Emulator for FPGA for Low-Power In-Edge Artificial Intelligence

Neuromorphic computing is a novel computational paradigm that draws inspiration from the structure and function of the human brain. Spiking Neural Networks (SNNs) are a promising approach for implementing energy-efficient Artificial Neural Networks (ANNs) in embedded systems. In this article, we present ModNEF, an open-source, neuromorphic digital hardware architecture designed for Field Programmable Gate Arrays (FPGAs). ModNEF is based on a modular architecture, where independent modules communicate via point-to-point connections to emulate SNNs. Our architecture offers two neuron models based on the Leaky Integrate and Fire (LIF) model, with a different emulation strategy. The modular nature of ModNEF allows researchers to extend the architecture by developing new modules to emulate different types of neurons or implement online learning rules. ModNEF is a clock-driven emulator, meaning that the neuron state is updated at regular intervals, even in the absence of input data. We evaluated the performance of the emulator using the MNIST and NMNIST datasets, with offline, full-precision training.