Tiny Machine Learning (TinyML) has become a growing field in on-device processing for Internet of Things (IoT) applications, capitalizing on AI algorithms that are optimized for their low complexity and energy efficiency. These algorithms are designed to minimize power and memory footprints, making them ideal for the constraints of IoT devices. Within this domain, Spiking Neural Networks (SNNs) stand out as a cutting-edge solution for TinyML, owning to their event-driven processing paradigm which offers an efficient method of handling dataflow. This paper presents a novel SNN architecture based on the 1st Order Leaky Integrate-and-Fire (LIF) neuron model to efficiently deploy vision-based ML algorithms on TinyML systems. A hardware-friendly LIF design is also proposed, and implemented on a Xilinx Artix-7 FPGA. To evaluate the proposed model, a collision avoidance dataset is considered as a case study. The proposed SNN model is compared to the state-of-the-art works and Binarized Convolutional Neural Network (BCNN) as a baseline. The results show the proposed approach is 86% more energy efficient than the baseline.

翻译：微型机器学习已成为物联网应用设备端处理领域日益增长的领域，其利用人工智能算法在低复杂度和高能效方面的优化优势。这些算法旨在最小化功耗和内存占用，使其非常适合物联网设备的资源约束条件。在该领域中，脉冲神经网络凭借其事件驱动的处理范式成为微型机器学习的前沿解决方案，为数据流处理提供了高效方法。本文提出一种基于一阶漏积分发放神经元模型的新型脉冲神经网络架构，旨在微型机器学习系统上高效部署基于视觉的机器学习算法。同时提出了一种硬件友好的LIF设计，并在Xilinx Artix-7 FPGA上实现。为评估所提模型，采用避障数据集作为案例进行研究。将所提出的脉冲神经网络模型与现有先进方案及作为基线的二值化卷积神经网络进行对比。结果表明，所提方法比基线模型能效提升86%。