汽车级微控制器上自动映射的神经网络特性
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers
摘要：
如今，神经网络代表了实现强大的深度学习算法的主要期望，该算法可以确定多个物理系统的行为和操作。建模、训练和运行所需的计算资源很大，特别是当涉及到神经网络通常需要泛化的数据量时。最新的TinyML技术允许在嵌入式系统上集成预先训练的模型，从而使边缘计算更快，更便宜，更安全。虽然这些技术起源于消费和工业领域，但许多行业都可以从中受益，例如汽车行业。在本文中，我们提出了一个框架，实现神经网络为基础的模型上的一个家庭的汽车微控制器，显示其效率在两个案例研究中应用于车辆：入侵检测控制器局域网总线和剩余容量估计锂离子电池，广泛应用于电动汽车。
英文摘要：
Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations. Computational resources required for model, training, and running are large, especially when related to the amount of data that Neural Networks typically need to generalize. The latest TinyML technologies allow integrating pre-trained models on embedded systems, allowing making computing at the edge faster, cheaper, and safer. Although these technologies originated in the consumer and industrial worlds, many sectors can greatly benefit from them, such as the automotive industry. In this paper, we present a framework for implementing Neural Network-based models on a family of automotive Microcontrollers, showing their efficiency in two case studies applied to vehicles: intrusion detection on the Controller Area Network bus and residual capacity estimation in Lithium-Ion batteries, widely used in Electric Vehicles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
