CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor
CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor
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DOI：
10.1109/ijcnn52387.2021.9533738
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
A. Viale;Alberto Marchisio;M. Martina;G. Masera;Muhammad Shafique
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文献类型：
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作者：
A. Viale;Alberto Marchisio;M. Martina;G. Masera;Muhammad Shafique
自动驾驶（AD）相关功能提供了新的移动形式，这也有利于其他类型的智能和自主系统，如机器人，智能交通和智能工业。对于这些应用，需要快速、实时地做出决策。此外，在寻求电动交通的过程中，这项任务必须遵循低功耗政策，而不会影响运输工具或机器人的自主性。这两个挑战可以使用新兴的尖峰神经网络（SNN）来解决。当部署在专门的神经形态硬件上时，SNN可以以低延迟和低功耗实现高性能。在本文中，我们使用连接到基于事件的相机的SNN来面对AD的关键问题之一，即，汽车和其他物体之间的分类。为了比传统的基于帧的相机消耗更少的功率，我们使用动态视觉传感器（DVS）[1]。实验遵循离线监督学习规则，然后将学习的SNN模型映射到英特尔Loihi Neuromorphic Research芯片上[2]。我们最好的实验在离线实现时达到了86%的准确率，当它被移植到Loihi芯片上时下降到83%。神经形态硬件实现对于每个样本具有最大0.72 ms的延迟，并且仅消耗310 mW。据我们所知，这项工作是在神经形态芯片上首次实现基于事件的汽车分类器。
Autonomous Driving (AD) related features provide new forms of mobility that are also beneficial for other kind of intelligent and autonomous systems like robots, smart transportation, and smart industries. For these applications, the decisions need to be made fast and in real-time. Moreover, in the quest for electric mobility, this task must follow low power policy, without affecting much the autonomy of the mean of transport or the robot. These two challenges can be tackled using the emerging Spiking Neural Networks (SNNs). When deployed on a specialized neuromorphic hardware, SNNs can achieve high performance with low latency and low power consumption. In this paper, we use an SNN connected to an event-based camera for facing one of the key problems for AD, i.e., the classification between cars and other objects. To consume less power than traditional frame-based cameras, we use a Dynamic Vision Sensor (DVS) [1]. The experiments are made following an offline supervised learning rule, followed by mapping the learnt SNN model on the Intel Loihi Neuromorphic Research Chip [2]. Our best experiment achieves an accuracy on offline implementation of 86%, that drops to 83% when it is ported onto the Loihi Chip. The Neuromorphic Hardware implementation has maximum 0.72 ms of latency for every sample, and consumes only 310 mW. To the best of our knowledge, this work is the first implementation of an event-based car classifier on a Neuromorphic Chip.