提出高效低功耗的脉冲神经网络硬件实现，适合物联网设备部署。
Energy-Aware FPGA Implementation of Spiking Neural Network with LIF Neurons
- 基于一阶漏电整合放电神经元设计可硬件部署的SNN架构。
- 在FPGA上实现，相比基线模型能效提升86%。
- 适用于资源受限的物联网视觉识别场景。
TinyML已成为物联网设备端处理的热门领域，依赖于低复杂度、高能效的AI算法。这些算法通过最小化功耗和内存占用，适应物联网设备的限制。其中，脉冲神经网络（SNN）因其事件驱动的数据流处理方式，在TinyML中表现突出。本文提出一种基于一阶漏电整合放电（LIF）神经元模型的新型SNN架构，用于在TinyML系统中高效部署视觉类机器学习算法。同时设计了一种适合硬件实现的LIF结构，并在Xilinx Artix-7 FPGA上完成实现。以碰撞避免数据集为案例进行评估，与当前先进方法及二值化卷积神经网络（BCNN）基线对比，结果表明所提方法比基线节能86%。
原文摘要 · Abstract (English)
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.
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