Efficient Spiking Neural Network Prototype on FPGA for Classifying Gases

Abstract

Spiking neural networks (SNNs) are numerical models whose computational properties come from interconnected biological neurons and synapses, leading to the development of neuromorphic systems in either software or hardware. These models in digital hardware operate in the discrete time domain, computing mainly the membrane potential of a neuron, where the arithmetic processes can be reduced to additions with minimal loss in accuracy. This advantage comes from approximating the solution of the original differential membrane equation by a recurrent discrete-time algorithm. In this work, we present a discrete-time SNN design using machine learning tools within the PyTorch framework. Its realization with a Field Programmable Gate Array (FPGA) can efficiently classify industrial gases, which are dangerous. It is demonstrated using available datasets created by experimental measurement boards with metal oxide gas sensors that show variability. The SNN model in this work was trained with the Adam optimizer and a weight clipping procedure to enhance convergence, where the classification efficiency was achieved at 95.7%. Then, the synaptic parameters were exported to an optimized FPGA. Three meaningful contributions of this digital neuromorphic system are as follows: (1) the proposed SNN approximates continuous membrane potentials in neurons with simple time series processing modules, (2) the neuron model, which is the Leaky Integrate and Fire (LIF) model with threshold capability, is parallelizable in hardware, and (3) the final SNN hardware prototype demonstrates its performance in terms of energy efficiency in a classification task scheme. Moreover, the final values of the weighting parameters between neurons are well-suited to an analog spiking system, serving as a hardware counterpart to the digital one in this work.